Program Listing for File Functions.h

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#pragma once

// @generated by torchgen/gen.py from Functions.h

#ifdef TORCH_ASSERT_NO_OPERATORS
#error This change adds a dependency on native_functions.yaml,            \
  meaning the file will need to be re-compiled every time an operator     \
  is changed or added. Consider if your change would be better placed in  \
  another file, or if a more specific header might achieve the same goal. \
  See NOTE: [Tensor vs. TensorBase]
#endif

#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS)
#error This change adds a dependency on all pytorch operators, meaning the     \
  file will need to be re-compiled every time an operator is changed or added. \
  Consider including a specific operator from <ATen/ops/{my_operator}.h> and   \
  see NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS].
#endif

// NOTE: [TORCH_ASSERT_ONLY_METHOD_OPERATORS]
//
// In ATen, certain generated headers files include the definitions of
// every single operator in PyTorch. Unfortunately this means every
// time an operator signature is updated or changed in
// native_functions.yaml, you (and every other PyTorch developer) need
// to recompile every source file that includes any of these headers.
//
// To break up these header dependencies, and improve incremental
// build times for all PyTorch developers. These headers are split
// into per-operator headers in the `ATen/ops` folder. This limits
// incremental builds to only changes to methods of `Tensor`, or files
// that use the specific operator being changed. With `at::sum` as an
// example, you should include
//
//   <ATen/ops/sum.h>               // instead of ATen/Functions.h
//   <ATen/ops/sum_native.h>        // instead of ATen/NativeFunctions.h
//   <ATen/ops/sum_ops.h>           // instead of ATen/Operators.h
//   <ATen/ops/sum_cpu_dispatch.h>  // instead of ATen/CPUFunctions.h
//
// However, even if you're careful to use this in your own code.
// `Functions.h` might be included indirectly through another header
// without you realising. To avoid this, you can add
//
//   #define TORCH_ASSERT_ONLY_METHOD_OPERATORS
//
// to the top of your source file. This way any time the non-specific
// headers are included, the compiler will error out.
//
// Also, be aware that `ops` are not available in all build
// configurations (namely fb-internal) so you must guard these
// includes with `#ifdef AT_PER_OPERATOR_HEADERS`. e.g.
//
//   #ifndef AT_PER_OPERATOR_HEADERS
//   #include <ATen/Functions.h>
//   #else
//   #include <ATen/ops/sum.h>
//   #endif

#include <ATen/Context.h>
#include <ATen/DeviceGuard.h>
#include <ATen/TensorUtils.h>
#include <ATen/TracerMode.h>
#include <ATen/core/Generator.h>
#include <ATen/core/Reduction.h>
#include <c10/core/SymInt.h>
#include <ATen/core/Tensor.h>
#include <c10/core/Scalar.h>
#include <c10/core/Storage.h>
#include <c10/core/TensorOptions.h>
#include <c10/util/Deprecated.h>
#include <optional>
#include <c10/util/OptionalArrayRef.h>

#include <ATen/ops/from_blob.h>
#include <ATen/ops/tensor.h>

#include <ATen/Operators.h>

namespace at {


// aten::_cast_Byte(Tensor self, bool non_blocking=False) -> Tensor
inline at::Tensor _cast_Byte(const at::Tensor & self, bool non_blocking=false) {
    return at::_ops::_cast_Byte::call(self, non_blocking);
}

// aten::_cast_Char(Tensor self, bool non_blocking=False) -> Tensor
inline at::Tensor _cast_Char(const at::Tensor & self, bool non_blocking=false) {
    return at::_ops::_cast_Char::call(self, non_blocking);
}

// aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor
inline at::Tensor _cast_Double(const at::Tensor & self, bool non_blocking=false) {
    return at::_ops::_cast_Double::call(self, non_blocking);
}

// aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor
inline at::Tensor _cast_Float(const at::Tensor & self, bool non_blocking=false) {
    return at::_ops::_cast_Float::call(self, non_blocking);
}

// aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor
inline at::Tensor _cast_Int(const at::Tensor & self, bool non_blocking=false) {
    return at::_ops::_cast_Int::call(self, non_blocking);
}

// aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor
inline at::Tensor _cast_Long(const at::Tensor & self, bool non_blocking=false) {
    return at::_ops::_cast_Long::call(self, non_blocking);
}

// aten::_cast_Short(Tensor self, bool non_blocking=False) -> Tensor
inline at::Tensor _cast_Short(const at::Tensor & self, bool non_blocking=false) {
    return at::_ops::_cast_Short::call(self, non_blocking);
}

// aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor
inline at::Tensor _cast_Half(const at::Tensor & self, bool non_blocking=false) {
    return at::_ops::_cast_Half::call(self, non_blocking);
}

// aten::_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a)
inline at::Tensor _make_dual(const at::Tensor & primal, const at::Tensor & tangent, int64_t level) {
    return at::_ops::_make_dual::call(primal, tangent, level);
}

// aten::_unpack_dual(Tensor(a) dual, int level) -> (Tensor(a) primal, Tensor tangent)
inline ::std::tuple<at::Tensor,at::Tensor> _unpack_dual(const at::Tensor & dual, int64_t level) {
    return at::_ops::_unpack_dual::call(dual, level);
}

// aten::_new_zeros_with_same_feature_meta(Tensor self, Tensor other, *, int self_num_batch_dims=0) -> Tensor
inline at::Tensor _new_zeros_with_same_feature_meta(const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims=0) {
    return at::_ops::_new_zeros_with_same_feature_meta::call(self, other, self_num_batch_dims);
}

// aten::_has_same_storage_numel(Tensor self, Tensor other) -> bool
inline bool _has_same_storage_numel(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::_has_same_storage_numel::call(self, other);
}

// aten::align_tensors(Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> align_tensors(at::TensorList tensors) {
    return at::_ops::align_tensors::call(tensors);
}

// aten::_assert_async(Tensor self) -> ()
inline void _assert_async(const at::Tensor & self) {
    return at::_ops::_assert_async::call(self);
}

// aten::_assert_async.msg(Tensor self, str assert_msg) -> ()
inline void _assert_async(const at::Tensor & self, c10::string_view assert_msg) {
    return at::_ops::_assert_async_msg::call(self, assert_msg);
}

// aten::_assert_scalar(Scalar self, str assert_msg) -> ()
inline void _assert_scalar(const at::Scalar & self, c10::string_view assert_msg) {
    return at::_ops::_assert_scalar::call(self, assert_msg);
}

// aten::_functional_assert_scalar(Scalar self, str assert_msg, Tensor dep_token) -> Tensor
inline at::Tensor _functional_assert_scalar(const at::Scalar & self, c10::string_view assert_msg, const at::Tensor & dep_token) {
    return at::_ops::_functional_assert_scalar::call(self, assert_msg, dep_token);
}

// aten::_functional_assert_async.msg(Tensor self, str assert_msg, Tensor dep_token) -> Tensor
inline at::Tensor _functional_assert_async(const at::Tensor & self, c10::string_view assert_msg, const at::Tensor & dep_token) {
    return at::_ops::_functional_assert_async_msg::call(self, assert_msg, dep_token);
}

// aten::_assert_tensor_metadata(Tensor a, SymInt[]? size=None, SymInt[]? stride=None, ScalarType? dtype=None) -> ()
inline void _assert_tensor_metadata(const at::Tensor & a, at::OptionalIntArrayRef size=::std::nullopt, at::OptionalIntArrayRef stride=::std::nullopt, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_assert_tensor_metadata::call(a, size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*size)) : ::std::nullopt, stride.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*stride)) : ::std::nullopt, dtype);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void _assert_tensor_metadata(const at::Tensor & a, at::OptionalIntArrayRef size=::std::nullopt, at::OptionalIntArrayRef stride=::std::nullopt, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_assert_tensor_metadata::call(a, size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*size)) : ::std::nullopt, stride.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*stride)) : ::std::nullopt, dtype);
  }
}

// aten::_assert_tensor_metadata(Tensor a, SymInt[]? size=None, SymInt[]? stride=None, ScalarType? dtype=None) -> ()
inline void _assert_tensor_metadata_symint(const at::Tensor & a, at::OptionalSymIntArrayRef size=::std::nullopt, at::OptionalSymIntArrayRef stride=::std::nullopt, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_assert_tensor_metadata::call(a, size, stride, dtype);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void _assert_tensor_metadata(const at::Tensor & a, at::OptionalSymIntArrayRef size=::std::nullopt, at::OptionalSymIntArrayRef stride=::std::nullopt, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_assert_tensor_metadata::call(a, size, stride, dtype);
  }
}

// aten::_print(str s) -> ()
inline void _print(c10::string_view s) {
    return at::_ops::_print::call(s);
}

// aten::sym_constrain_range(Scalar size, *, int? min=None, int? max=None) -> ()
inline void sym_constrain_range(const at::Scalar & size, ::std::optional<int64_t> min=::std::nullopt, ::std::optional<int64_t> max=::std::nullopt) {
    return at::_ops::sym_constrain_range::call(size, min, max);
}

// aten::sym_constrain_range_for_size(Scalar size, *, int? min=None, int? max=None) -> ()
inline void sym_constrain_range_for_size(const at::Scalar & size, ::std::optional<int64_t> min=::std::nullopt, ::std::optional<int64_t> max=::std::nullopt) {
    return at::_ops::sym_constrain_range_for_size::call(size, min, max);
}

// aten::_functional_sym_constrain_range(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor
inline at::Tensor _functional_sym_constrain_range(const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max, const at::Tensor & dep_token) {
    return at::_ops::_functional_sym_constrain_range::call(size, min, max, dep_token);
}

// aten::_functional_sym_constrain_range_for_size(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor
inline at::Tensor _functional_sym_constrain_range_for_size(const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max, const at::Tensor & dep_token) {
    return at::_ops::_functional_sym_constrain_range_for_size::call(size, min, max, dep_token);
}

// aten::_make_dep_token(*, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor _make_dep_token(at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::_make_dep_token::call(c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::_make_dep_token(*, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor _make_dep_token(::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_make_dep_token::call(dtype, layout, device, pin_memory, memory_format);
}

// aten::_use_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank) -> bool
inline bool _use_cudnn_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank) {
    return at::_ops::_use_cudnn_ctc_loss::call(log_probs, targets, input_lengths, target_lengths, blank);
}

// aten::_use_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank) -> bool
inline bool _use_cudnn_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank) {
    return at::_ops::_use_cudnn_ctc_loss_Tensor::call(log_probs, targets, input_lengths, target_lengths, blank);
}

// aten::_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _cudnn_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity) {
    return at::_ops::_cudnn_ctc_loss::call(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity);
}

// aten::_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _cudnn_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool deterministic, bool zero_infinity) {
    return at::_ops::_cudnn_ctc_loss_Tensor::call(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity);
}

// aten::_use_cudnn_rnn_flatten_weight() -> bool
inline bool _use_cudnn_rnn_flatten_weight() {
    return at::_ops::_use_cudnn_rnn_flatten_weight::call();
}

// aten::_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor
inline at::Tensor _cudnn_rnn_flatten_weight(at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional) {
    return at::_ops::_cudnn_rnn_flatten_weight::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _cudnn_rnn_flatten_weight(at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional) {
    return at::_ops::_cudnn_rnn_flatten_weight::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional);
  }
}

// aten::_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor
inline at::Tensor _cudnn_rnn_flatten_weight_symint(at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) {
    return at::_ops::_cudnn_rnn_flatten_weight::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _cudnn_rnn_flatten_weight(at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) {
    return at::_ops::_cudnn_rnn_flatten_weight::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional);
  }
}

// aten::_cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state);
  }
}

// aten::_cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn_symint(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _cudnn_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state);
  }
}

// aten::_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> _cudnn_rnn_backward(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::_cudnn_rnn_backward::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> _cudnn_rnn_backward(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::_cudnn_rnn_backward::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask);
  }
}

// aten::_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> _cudnn_rnn_backward_symint(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::_cudnn_rnn_backward::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> _cudnn_rnn_backward(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::_cudnn_rnn_backward::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask);
  }
}

// aten::_cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, at::TensorOptions options) {
    return at::_ops::_cudnn_init_dropout_state::call(dropout, train, dropout_seed, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::_cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_cudnn_init_dropout_state::call(dropout, train, dropout_seed, dtype, layout, device, pin_memory);
}

// aten::_debug_has_internal_overlap(Tensor self) -> int
inline int64_t _debug_has_internal_overlap(const at::Tensor & self) {
    return at::_ops::_debug_has_internal_overlap::call(self);
}

// aten::_fused_dropout(Tensor self, float p, Generator? generator=None) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _fused_dropout(const at::Tensor & self, double p, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::_fused_dropout::call(self, p, generator);
}

// aten::_masked_scale(Tensor self, Tensor mask, float scale) -> Tensor
inline at::Tensor _masked_scale(const at::Tensor & self, const at::Tensor & mask, double scale) {
    return at::_ops::_masked_scale::call(self, mask, scale);
}

// aten::native_dropout(Tensor input, float p, bool? train) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> native_dropout(const at::Tensor & input, double p, ::std::optional<bool> train) {
    return at::_ops::native_dropout::call(input, p, train);
}

// aten::native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor
inline at::Tensor native_dropout_backward(const at::Tensor & grad_output, const at::Tensor & mask, double scale) {
    return at::_ops::native_dropout_backward::call(grad_output, mask, scale);
}

// aten::_sobol_engine_draw(Tensor quasi, int n, Tensor sobolstate, int dimension, int num_generated, ScalarType? dtype) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _sobol_engine_draw(const at::Tensor & quasi, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated, ::std::optional<at::ScalarType> dtype) {
    return at::_ops::_sobol_engine_draw::call(quasi, n, sobolstate, dimension, num_generated, dtype);
}

// aten::_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!)
inline at::Tensor & _sobol_engine_ff_(at::Tensor & self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated) {
    return at::_ops::_sobol_engine_ff_::call(self, n, sobolstate, dimension, num_generated);
}

// aten::_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!)
inline at::Tensor & _sobol_engine_scramble_(at::Tensor & self, const at::Tensor & ltm, int64_t dimension) {
    return at::_ops::_sobol_engine_scramble_::call(self, ltm, dimension);
}

// aten::_sobol_engine_initialize_state_(Tensor(a!) self, int dimension) -> Tensor(a!)
inline at::Tensor & _sobol_engine_initialize_state_(at::Tensor & self, int64_t dimension) {
    return at::_ops::_sobol_engine_initialize_state_::call(self, dimension);
}

// aten::_reshape_from_tensor(Tensor self, Tensor shape) -> Tensor
inline at::Tensor _reshape_from_tensor(const at::Tensor & self, const at::Tensor & shape) {
    return at::_ops::_reshape_from_tensor::call(self, shape);
}

// aten::_shape_as_tensor(Tensor self) -> Tensor
inline at::Tensor _shape_as_tensor(const at::Tensor & self) {
    return at::_ops::_shape_as_tensor::call(self);
}

// aten::dropout(Tensor input, float p, bool train) -> Tensor
inline at::Tensor dropout(const at::Tensor & input, double p, bool train) {
    return at::_ops::dropout::call(input, p, train);
}

// aten::dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
inline at::Tensor & dropout_(at::Tensor & self, double p, bool train) {
    return at::_ops::dropout_::call(self, p, train);
}

// aten::feature_dropout(Tensor input, float p, bool train) -> Tensor
inline at::Tensor feature_dropout(const at::Tensor & input, double p, bool train) {
    return at::_ops::feature_dropout::call(input, p, train);
}

// aten::feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
inline at::Tensor & feature_dropout_(at::Tensor & self, double p, bool train) {
    return at::_ops::feature_dropout_::call(self, p, train);
}

// aten::alpha_dropout(Tensor input, float p, bool train) -> Tensor
inline at::Tensor alpha_dropout(const at::Tensor & input, double p, bool train) {
    return at::_ops::alpha_dropout::call(input, p, train);
}

// aten::alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
inline at::Tensor & alpha_dropout_(at::Tensor & self, double p, bool train) {
    return at::_ops::alpha_dropout_::call(self, p, train);
}

// aten::feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor
inline at::Tensor feature_alpha_dropout(const at::Tensor & input, double p, bool train) {
    return at::_ops::feature_alpha_dropout::call(input, p, train);
}

// aten::feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
inline at::Tensor & feature_alpha_dropout_(at::Tensor & self, double p, bool train) {
    return at::_ops::feature_alpha_dropout_::call(self, p, train);
}

// aten::abs(Tensor self) -> Tensor
inline at::Tensor abs(const at::Tensor & self) {
    return at::_ops::abs::call(self);
}

// aten::abs_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & abs_(at::Tensor & self) {
    return at::_ops::abs_::call(self);
}

// aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & abs_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::abs_out::call(self, out);
}
// aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & abs_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::abs_out::call(self, out);
}

// aten::absolute(Tensor self) -> Tensor
inline at::Tensor absolute(const at::Tensor & self) {
    return at::_ops::absolute::call(self);
}

// aten::absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & absolute_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::absolute_out::call(self, out);
}
// aten::absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & absolute_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::absolute_out::call(self, out);
}

// aten::angle(Tensor self) -> Tensor
inline at::Tensor angle(const at::Tensor & self) {
    return at::_ops::angle::call(self);
}

// aten::angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & angle_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::angle_out::call(self, out);
}
// aten::angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & angle_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::angle_out::call(self, out);
}

// aten::view_as_real(Tensor(a) self) -> Tensor(a)
inline at::Tensor view_as_real(const at::Tensor & self) {
    return at::_ops::view_as_real::call(self);
}

// aten::view_as_complex(Tensor(a) self) -> Tensor(a)
inline at::Tensor view_as_complex(const at::Tensor & self) {
    return at::_ops::view_as_complex::call(self);
}

// aten::sgn(Tensor self) -> Tensor
inline at::Tensor sgn(const at::Tensor & self) {
    return at::_ops::sgn::call(self);
}

// aten::sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sgn_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::sgn_out::call(self, out);
}
// aten::sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sgn_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::sgn_out::call(self, out);
}

// aten::real(Tensor(a) self) -> Tensor(a)
inline at::Tensor real(const at::Tensor & self) {
    return at::_ops::real::call(self);
}

// aten::imag(Tensor(a) self) -> Tensor(a)
inline at::Tensor imag(const at::Tensor & self) {
    return at::_ops::imag::call(self);
}

// aten::_conj(Tensor(a) self) -> Tensor(a)
inline at::Tensor _conj(const at::Tensor & self) {
    return at::_ops::_conj::call(self);
}

// aten::conj(Tensor(a) self) -> Tensor(a)
inline at::Tensor __dispatch_conj(const at::Tensor & self) {
    return at::_ops::conj::call(self);
}

// aten::_conj_physical(Tensor self) -> Tensor
inline at::Tensor _conj_physical(const at::Tensor & self) {
    return at::_ops::_conj_physical::call(self);
}

// aten::conj_physical(Tensor self) -> Tensor
inline at::Tensor conj_physical(const at::Tensor & self) {
    return at::_ops::conj_physical::call(self);
}

// aten::conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & conj_physical_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::conj_physical_out::call(self, out);
}
// aten::conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & conj_physical_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::conj_physical_out::call(self, out);
}

// aten::conj_physical_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & conj_physical_(at::Tensor & self) {
    return at::_ops::conj_physical_::call(self);
}

// aten::resolve_conj(Tensor(a) self) -> Tensor(a)
inline at::Tensor resolve_conj(const at::Tensor & self) {
    return at::_ops::resolve_conj::call(self);
}

// aten::resolve_neg(Tensor(a) self) -> Tensor(a)
inline at::Tensor resolve_neg(const at::Tensor & self) {
    return at::_ops::resolve_neg::call(self);
}

// aten::_neg_view(Tensor(a) self) -> Tensor(a)
inline at::Tensor _neg_view(const at::Tensor & self) {
    return at::_ops::_neg_view::call(self);
}

// aten::acos(Tensor self) -> Tensor
inline at::Tensor acos(const at::Tensor & self) {
    return at::_ops::acos::call(self);
}

// aten::acos_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & acos_(at::Tensor & self) {
    return at::_ops::acos_::call(self);
}

// aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & acos_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::acos_out::call(self, out);
}
// aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & acos_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::acos_out::call(self, out);
}

// aten::arccos(Tensor self) -> Tensor
inline at::Tensor arccos(const at::Tensor & self) {
    return at::_ops::arccos::call(self);
}

// aten::arccos_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & arccos_(at::Tensor & self) {
    return at::_ops::arccos_::call(self);
}

// aten::arccos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arccos_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::arccos_out::call(self, out);
}
// aten::arccos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arccos_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::arccos_out::call(self, out);
}

// aten::avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, bool ceil_mode=False, bool count_include_pad=True) -> Tensor
inline at::Tensor avg_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true) {
    return at::_ops::avg_pool1d::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad);
}

// aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor
inline at::Tensor adaptive_avg_pool1d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool1d::call(self, output_size);
}

// aten::adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool1d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_max_pool1d::call(self, output_size);
}

// aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
inline at::Tensor add(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::add_Tensor::call(self, other, alpha);
}

// aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & add_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::add_out::call(self, other, alpha, out);
}
// aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & add_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::add_out::call(self, other, alpha, out);
}

// aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
inline at::Tensor _add_relu(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::_add_relu_Tensor::call(self, other, alpha);
}

// aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)
inline at::Tensor & _add_relu_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::_add_relu__Tensor::call(self, other, alpha);
}

// aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::_add_relu_out::call(self, other, alpha, out);
}
// aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _add_relu_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::_add_relu_out::call(self, other, alpha, out);
}

// aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
inline at::Tensor _add_relu(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::_add_relu_Scalar::call(self, other, alpha);
}

// aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)
inline at::Tensor & _add_relu_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::_add_relu__Scalar::call(self, other, alpha);
}

// aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
inline at::Tensor add(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::add_Scalar::call(self, other, alpha);
}

// aten::addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inline at::Tensor addmv(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addmv::call(self, mat, vec, beta, alpha);
}

// aten::addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!)
inline at::Tensor & addmv_(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addmv_::call(self, mat, vec, beta, alpha);
}

// aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addmv_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addmv_out::call(self, mat, vec, beta, alpha, out);
}
// aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addmv_outf(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::addmv_out::call(self, mat, vec, beta, alpha, out);
}

// aten::addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inline at::Tensor addr(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addr::call(self, vec1, vec2, beta, alpha);
}

// aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addr_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addr_out::call(self, vec1, vec2, beta, alpha, out);
}
// aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addr_outf(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::addr_out::call(self, vec1, vec2, beta, alpha, out);
}

// aten::affine_grid_generator(Tensor theta, SymInt[] size, bool align_corners) -> Tensor
inline at::Tensor affine_grid_generator(const at::Tensor & theta, at::IntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator::call(theta, c10::fromIntArrayRefSlow(size), align_corners);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor affine_grid_generator(const at::Tensor & theta, at::IntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator::call(theta, c10::fromIntArrayRefSlow(size), align_corners);
  }
}

// aten::affine_grid_generator(Tensor theta, SymInt[] size, bool align_corners) -> Tensor
inline at::Tensor affine_grid_generator_symint(const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator::call(theta, size, align_corners);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor affine_grid_generator(const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator::call(theta, size, align_corners);
  }
}

// aten::affine_grid_generator_backward(Tensor grad, SymInt[] size, bool align_corners) -> Tensor
inline at::Tensor affine_grid_generator_backward(const at::Tensor & grad, at::IntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator_backward::call(grad, c10::fromIntArrayRefSlow(size), align_corners);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor affine_grid_generator_backward(const at::Tensor & grad, at::IntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator_backward::call(grad, c10::fromIntArrayRefSlow(size), align_corners);
  }
}

// aten::affine_grid_generator_backward(Tensor grad, SymInt[] size, bool align_corners) -> Tensor
inline at::Tensor affine_grid_generator_backward_symint(const at::Tensor & grad, c10::SymIntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator_backward::call(grad, size, align_corners);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor affine_grid_generator_backward(const at::Tensor & grad, c10::SymIntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator_backward::call(grad, size, align_corners);
  }
}

// aten::_is_all_true(Tensor self) -> Tensor
inline at::Tensor _is_all_true(const at::Tensor & self) {
    return at::_ops::_is_all_true::call(self);
}

// aten::_is_any_true(Tensor self) -> Tensor
inline at::Tensor _is_any_true(const at::Tensor & self) {
    return at::_ops::_is_any_true::call(self);
}

// aten::_test_check_tensor(Tensor self) -> Tensor
inline at::Tensor _test_check_tensor(const at::Tensor & self) {
    return at::_ops::_test_check_tensor::call(self);
}

// aten::_test_functorch_fallback(Tensor self, Tensor other) -> Tensor
inline at::Tensor _test_functorch_fallback(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::_test_functorch_fallback::call(self, other);
}

// aten::all.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
inline at::Tensor all(const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::all_dim::call(self, dim, keepdim);
}

// aten::all.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor
inline at::Tensor all(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) {
    return at::_ops::all_dims::call(self, dim, keepdim);
}

// aten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & all_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::all_out::call(self, dim, keepdim, out);
}
// aten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & all_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) {
    return at::_ops::all_out::call(self, dim, keepdim, out);
}

// aten::all.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & all_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) {
    return at::_ops::all_dims_out::call(self, dim, keepdim, out);
}
// aten::all.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & all_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::all_dims_out::call(self, dim, keepdim, out);
}

// aten::all.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor
inline at::Tensor all(const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::all_dimname::call(self, dim, keepdim);
}

// aten::all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & all_out(at::Tensor & out, const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::all_dimname_out::call(self, dim, keepdim, out);
}
// aten::all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & all_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out) {
    return at::_ops::all_dimname_out::call(self, dim, keepdim, out);
}

// aten::allclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> bool
inline bool allclose(const at::Tensor & self, const at::Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false) {
    return at::_ops::allclose::call(self, other, rtol, atol, equal_nan);
}

// aten::any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor
inline at::Tensor any(const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::any_dim::call(self, dim, keepdim);
}

// aten::any.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor
inline at::Tensor any(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) {
    return at::_ops::any_dims::call(self, dim, keepdim);
}

// aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & any_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::any_out::call(self, dim, keepdim, out);
}
// aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & any_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) {
    return at::_ops::any_out::call(self, dim, keepdim, out);
}

// aten::any.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & any_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) {
    return at::_ops::any_dims_out::call(self, dim, keepdim, out);
}
// aten::any.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & any_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::any_dims_out::call(self, dim, keepdim, out);
}

// aten::any.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor
inline at::Tensor any(const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::any_dimname::call(self, dim, keepdim);
}

// aten::any.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & any_out(at::Tensor & out, const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::any_dimname_out::call(self, dim, keepdim, out);
}
// aten::any.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & any_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out) {
    return at::_ops::any_dimname_out::call(self, dim, keepdim, out);
}

// aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor arange(const at::Scalar & end, at::TensorOptions options={}) {
    return at::_ops::arange::call(end, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor arange(const at::Scalar & end, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::arange::call(end, dtype, layout, device, pin_memory);
}

// aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor arange(const at::Scalar & start, const at::Scalar & end, at::TensorOptions options={}) {
    return at::_ops::arange_start::call(start, end, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor arange(const at::Scalar & start, const at::Scalar & end, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::arange_start::call(start, end, dtype, layout, device, pin_memory);
}

// aten::arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor arange(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::TensorOptions options={}) {
    return at::_ops::arange_start_step::call(start, end, step, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor arange(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::arange_start_step::call(start, end, step, dtype, layout, device, pin_memory);
}

// aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arange_out(at::Tensor & out, const at::Scalar & end) {
    return at::_ops::arange_out::call(end, out);
}
// aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arange_outf(const at::Scalar & end, at::Tensor & out) {
    return at::_ops::arange_out::call(end, out);
}

// aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arange_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step) {
    return at::_ops::arange_start_out::call(start, end, step, out);
}
// aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arange_outf(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) {
    return at::_ops::arange_start_out::call(start, end, step, out);
}

// aten::_dim_arange(Tensor like, int dim) -> Tensor
inline at::Tensor _dim_arange(const at::Tensor & like, int64_t dim) {
    return at::_ops::_dim_arange::call(like, dim);
}

// aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
inline at::Tensor argmax(const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false) {
    return at::_ops::argmax::call(self, dim, keepdim);
}

// aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & argmax_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false) {
    return at::_ops::argmax_out::call(self, dim, keepdim, out);
}
// aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & argmax_outf(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & out) {
    return at::_ops::argmax_out::call(self, dim, keepdim, out);
}

// aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
inline at::Tensor argmin(const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false) {
    return at::_ops::argmin::call(self, dim, keepdim);
}

// aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & argmin_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false) {
    return at::_ops::argmin_out::call(self, dim, keepdim, out);
}
// aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & argmin_outf(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & out) {
    return at::_ops::argmin_out::call(self, dim, keepdim, out);
}

// aten::acosh(Tensor self) -> Tensor
inline at::Tensor acosh(const at::Tensor & self) {
    return at::_ops::acosh::call(self);
}

// aten::acosh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & acosh_(at::Tensor & self) {
    return at::_ops::acosh_::call(self);
}

// aten::acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & acosh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::acosh_out::call(self, out);
}
// aten::acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & acosh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::acosh_out::call(self, out);
}

// aten::arccosh(Tensor self) -> Tensor
inline at::Tensor arccosh(const at::Tensor & self) {
    return at::_ops::arccosh::call(self);
}

// aten::arccosh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & arccosh_(at::Tensor & self) {
    return at::_ops::arccosh_::call(self);
}

// aten::arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arccosh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::arccosh_out::call(self, out);
}
// aten::arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arccosh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::arccosh_out::call(self, out);
}

// aten::asinh(Tensor self) -> Tensor
inline at::Tensor asinh(const at::Tensor & self) {
    return at::_ops::asinh::call(self);
}

// aten::asinh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & asinh_(at::Tensor & self) {
    return at::_ops::asinh_::call(self);
}

// aten::asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & asinh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::asinh_out::call(self, out);
}
// aten::asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & asinh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::asinh_out::call(self, out);
}

// aten::arcsinh(Tensor self) -> Tensor
inline at::Tensor arcsinh(const at::Tensor & self) {
    return at::_ops::arcsinh::call(self);
}

// aten::arcsinh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & arcsinh_(at::Tensor & self) {
    return at::_ops::arcsinh_::call(self);
}

// aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arcsinh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::arcsinh_out::call(self, out);
}
// aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arcsinh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::arcsinh_out::call(self, out);
}

// aten::atanh(Tensor self) -> Tensor
inline at::Tensor atanh(const at::Tensor & self) {
    return at::_ops::atanh::call(self);
}

// aten::atanh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & atanh_(at::Tensor & self) {
    return at::_ops::atanh_::call(self);
}

// aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & atanh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::atanh_out::call(self, out);
}
// aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & atanh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::atanh_out::call(self, out);
}

// aten::arctanh(Tensor self) -> Tensor
inline at::Tensor arctanh(const at::Tensor & self) {
    return at::_ops::arctanh::call(self);
}

// aten::arctanh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & arctanh_(at::Tensor & self) {
    return at::_ops::arctanh_::call(self);
}

// aten::arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arctanh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::arctanh_out::call(self, out);
}
// aten::arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arctanh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::arctanh_out::call(self, out);
}

// aten::as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)
inline at::Tensor as_strided(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor as_strided(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt);
  }
}

// aten::as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)
inline at::Tensor as_strided_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided::call(self, size, stride, storage_offset);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor as_strided(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided::call(self, size, stride, storage_offset);
  }
}

// aten::as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!)
inline const at::Tensor & as_strided_(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & as_strided_(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt);
  }
}

// aten::as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!)
inline const at::Tensor & as_strided__symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_::call(self, size, stride, storage_offset);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & as_strided_(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_::call(self, size, stride, storage_offset);
  }
}

// aten::asin(Tensor self) -> Tensor
inline at::Tensor asin(const at::Tensor & self) {
    return at::_ops::asin::call(self);
}

// aten::asin_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & asin_(at::Tensor & self) {
    return at::_ops::asin_::call(self);
}

// aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & asin_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::asin_out::call(self, out);
}
// aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & asin_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::asin_out::call(self, out);
}

// aten::arcsin(Tensor self) -> Tensor
inline at::Tensor arcsin(const at::Tensor & self) {
    return at::_ops::arcsin::call(self);
}

// aten::arcsin_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & arcsin_(at::Tensor & self) {
    return at::_ops::arcsin_::call(self);
}

// aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arcsin_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::arcsin_out::call(self, out);
}
// aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arcsin_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::arcsin_out::call(self, out);
}

// aten::atan(Tensor self) -> Tensor
inline at::Tensor atan(const at::Tensor & self) {
    return at::_ops::atan::call(self);
}

// aten::atan_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & atan_(at::Tensor & self) {
    return at::_ops::atan_::call(self);
}

// aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & atan_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::atan_out::call(self, out);
}
// aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & atan_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::atan_out::call(self, out);
}

// aten::arctan(Tensor self) -> Tensor
inline at::Tensor arctan(const at::Tensor & self) {
    return at::_ops::arctan::call(self);
}

// aten::arctan_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & arctan_(at::Tensor & self) {
    return at::_ops::arctan_::call(self);
}

// aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arctan_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::arctan_out::call(self, out);
}
// aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arctan_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::arctan_out::call(self, out);
}

// aten::atleast_1d(Tensor self) -> Tensor
inline at::Tensor atleast_1d(const at::Tensor & self) {
    return at::_ops::atleast_1d::call(self);
}

// aten::atleast_1d.Sequence(Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> atleast_1d(at::TensorList tensors) {
    return at::_ops::atleast_1d_Sequence::call(tensors);
}

// aten::atleast_2d(Tensor self) -> Tensor
inline at::Tensor atleast_2d(const at::Tensor & self) {
    return at::_ops::atleast_2d::call(self);
}

// aten::atleast_2d.Sequence(Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> atleast_2d(at::TensorList tensors) {
    return at::_ops::atleast_2d_Sequence::call(tensors);
}

// aten::atleast_3d(Tensor self) -> Tensor
inline at::Tensor atleast_3d(const at::Tensor & self) {
    return at::_ops::atleast_3d::call(self);
}

// aten::atleast_3d.Sequence(Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> atleast_3d(at::TensorList tensors) {
    return at::_ops::atleast_3d_Sequence::call(tensors);
}

// aten::baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inline at::Tensor baddbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::baddbmm::call(self, batch1, batch2, beta, alpha);
}

// aten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & baddbmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::baddbmm_out::call(self, batch1, batch2, beta, alpha, out);
}
// aten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & baddbmm_outf(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::baddbmm_out::call(self, batch1, batch2, beta, alpha, out);
}

// aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor bartlett_window(int64_t window_length, at::TensorOptions options={}) {
    return at::_ops::bartlett_window::call(window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor bartlett_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::bartlett_window::call(window_length, dtype, layout, device, pin_memory);
}

// aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor bartlett_window(int64_t window_length, bool periodic, at::TensorOptions options={}) {
    return at::_ops::bartlett_window_periodic::call(window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor bartlett_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::bartlett_window_periodic::call(window_length, periodic, dtype, layout, device, pin_memory);
}

// aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor
inline at::Tensor batch_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps, bool cudnn_enabled) {
    return at::_ops::batch_norm::call(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled);
}

// aten::quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor
inline at::Tensor quantized_batch_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point) {
    return at::_ops::quantized_batch_norm::call(input, weight, bias, mean, var, eps, output_scale, output_zero_point);
}

// aten::_batch_norm_impl_index(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> (Tensor, Tensor, Tensor, Tensor, int)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,int64_t> _batch_norm_impl_index(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps, bool cudnn_enabled) {
    return at::_ops::_batch_norm_impl_index::call(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled);
}

// aten::_batch_norm_impl_index_backward(int impl_index, Tensor input, Tensor grad_output, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var_transform, bool train, float eps, bool[3] output_mask, Tensor reservedSpace) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _batch_norm_impl_index_backward(int64_t impl_index, const at::Tensor & input, const at::Tensor & grad_output, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var_transform, bool train, double eps, ::std::array<bool,3> output_mask, const at::Tensor & reservedSpace) {
    return at::_ops::_batch_norm_impl_index_backward::call(impl_index, input, grad_output, weight, running_mean, running_var, save_mean, save_var_transform, train, eps, output_mask, reservedSpace);
}

// aten::bernoulli(Tensor self, *, Generator? generator=None) -> Tensor
inline at::Tensor bernoulli(const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::bernoulli::call(self, generator);
}

// aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bernoulli_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::bernoulli_out::call(self, generator, out);
}
// aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bernoulli_outf(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::bernoulli_out::call(self, generator, out);
}

// aten::bernoulli.p(Tensor self, float p, *, Generator? generator=None) -> Tensor
inline at::Tensor bernoulli(const at::Tensor & self, double p, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::bernoulli_p::call(self, p, generator);
}

// aten::bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None) -> Tensor
inline at::Tensor bilinear(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}) {
    return at::_ops::bilinear::call(input1, input2, weight, bias);
}

// aten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor
inline at::Tensor binary_cross_entropy(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::binary_cross_entropy::call(self, target, weight, reduction);
}

// aten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & binary_cross_entropy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::binary_cross_entropy_out::call(self, target, weight, reduction, out);
}
// aten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & binary_cross_entropy_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) {
    return at::_ops::binary_cross_entropy_out::call(self, target, weight, reduction, out);
}

// aten::binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor
inline at::Tensor binary_cross_entropy_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::binary_cross_entropy_backward::call(grad_output, self, target, weight, reduction);
}

// aten::binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & binary_cross_entropy_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::binary_cross_entropy_backward_grad_input::call(grad_output, self, target, weight, reduction, grad_input);
}
// aten::binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & binary_cross_entropy_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input) {
    return at::_ops::binary_cross_entropy_backward_grad_input::call(grad_output, self, target, weight, reduction, grad_input);
}

// aten::binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor
inline at::Tensor binary_cross_entropy_with_logits(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, const ::std::optional<at::Tensor> & pos_weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::binary_cross_entropy_with_logits::call(self, target, weight, pos_weight, reduction);
}

// aten::bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensor
inline at::Tensor bincount(const at::Tensor & self, const ::std::optional<at::Tensor> & weights={}, int64_t minlength=0) {
    return at::_ops::bincount::call(self, weights, minlength);
}

// aten::bitwise_not(Tensor self) -> Tensor
inline at::Tensor bitwise_not(const at::Tensor & self) {
    return at::_ops::bitwise_not::call(self);
}

// aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_not_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::bitwise_not_out::call(self, out);
}
// aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_not_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::bitwise_not_out::call(self, out);
}

// aten::copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & copysign_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::copysign_out::call(self, other, out);
}
// aten::copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & copysign_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::copysign_out::call(self, other, out);
}

// aten::copysign.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor copysign(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::copysign_Tensor::call(self, other);
}

// aten::copysign.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor copysign(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::copysign_Scalar::call(self, other);
}

// aten::copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & copysign_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::copysign_Scalar_out::call(self, other, out);
}
// aten::copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & copysign_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::copysign_Scalar_out::call(self, other, out);
}

// aten::_lazy_clone(Tensor self) -> Tensor
inline at::Tensor _lazy_clone(const at::Tensor & self) {
    return at::_ops::_lazy_clone::call(self);
}

// aten::logical_not(Tensor self) -> Tensor
inline at::Tensor logical_not(const at::Tensor & self) {
    return at::_ops::logical_not::call(self);
}

// aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logical_not_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::logical_not_out::call(self, out);
}
// aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logical_not_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::logical_not_out::call(self, out);
}

// aten::logical_xor(Tensor self, Tensor other) -> Tensor
inline at::Tensor logical_xor(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logical_xor::call(self, other);
}

// aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logical_xor_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logical_xor_out::call(self, other, out);
}
// aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logical_xor_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::logical_xor_out::call(self, other, out);
}

// aten::logical_and(Tensor self, Tensor other) -> Tensor
inline at::Tensor logical_and(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logical_and::call(self, other);
}

// aten::logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logical_and_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logical_and_out::call(self, other, out);
}
// aten::logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logical_and_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::logical_and_out::call(self, other, out);
}

// aten::logical_or(Tensor self, Tensor other) -> Tensor
inline at::Tensor logical_or(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logical_or::call(self, other);
}

// aten::logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logical_or_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logical_or_out::call(self, other, out);
}
// aten::logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logical_or_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::logical_or_out::call(self, other, out);
}

// aten::blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor blackman_window(int64_t window_length, at::TensorOptions options={}) {
    return at::_ops::blackman_window::call(window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor blackman_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::blackman_window::call(window_length, dtype, layout, device, pin_memory);
}

// aten::blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor blackman_window(int64_t window_length, bool periodic, at::TensorOptions options={}) {
    return at::_ops::blackman_window_periodic::call(window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor blackman_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::blackman_window_periodic::call(window_length, periodic, dtype, layout, device, pin_memory);
}

// aten::bmm(Tensor self, Tensor mat2) -> Tensor
inline at::Tensor bmm(const at::Tensor & self, const at::Tensor & mat2) {
    return at::_ops::bmm::call(self, mat2);
}

// aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) {
    return at::_ops::bmm_out::call(self, mat2, out);
}
// aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bmm_outf(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) {
    return at::_ops::bmm_out::call(self, mat2, out);
}

// aten::broadcast_tensors(Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> broadcast_tensors(at::TensorList tensors) {
    return at::_ops::broadcast_tensors::call(tensors);
}

// aten::broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a)
inline at::Tensor broadcast_to(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::broadcast_to::call(self, c10::fromIntArrayRefSlow(size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor broadcast_to(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::broadcast_to::call(self, c10::fromIntArrayRefSlow(size));
  }
}

// aten::broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a)
inline at::Tensor broadcast_to_symint(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::broadcast_to::call(self, size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor broadcast_to(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::broadcast_to::call(self, size);
  }
}

// aten::_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a)
inline at::Tensor _sparse_broadcast_to(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_sparse_broadcast_to::call(self, size);
}

// aten::cat(Tensor[] tensors, int dim=0) -> Tensor
inline at::Tensor cat(const at::ITensorListRef & tensors, int64_t dim=0) {
    return at::_ops::cat::call(tensors, dim);
}

// aten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cat_out(at::Tensor & out, const at::ITensorListRef & tensors, int64_t dim=0) {
    return at::_ops::cat_out::call(tensors, dim, out);
}
// aten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cat_outf(const at::ITensorListRef & tensors, int64_t dim, at::Tensor & out) {
    return at::_ops::cat_out::call(tensors, dim, out);
}

// aten::cat.names(Tensor[] tensors, Dimname dim) -> Tensor
inline at::Tensor cat(at::TensorList tensors, at::Dimname dim) {
    return at::_ops::cat_names::call(tensors, dim);
}

// aten::cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cat_out(at::Tensor & out, at::TensorList tensors, at::Dimname dim) {
    return at::_ops::cat_names_out::call(tensors, dim, out);
}
// aten::cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cat_outf(at::TensorList tensors, at::Dimname dim, at::Tensor & out) {
    return at::_ops::cat_names_out::call(tensors, dim, out);
}

// aten::concat(Tensor[] tensors, int dim=0) -> Tensor
inline at::Tensor concat(at::TensorList tensors, int64_t dim=0) {
    return at::_ops::concat::call(tensors, dim);
}

// aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & concat_out(at::Tensor & out, at::TensorList tensors, int64_t dim=0) {
    return at::_ops::concat_out::call(tensors, dim, out);
}
// aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & concat_outf(at::TensorList tensors, int64_t dim, at::Tensor & out) {
    return at::_ops::concat_out::call(tensors, dim, out);
}

// aten::concat.names(Tensor[] tensors, Dimname dim) -> Tensor
inline at::Tensor concat(at::TensorList tensors, at::Dimname dim) {
    return at::_ops::concat_names::call(tensors, dim);
}

// aten::concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & concat_out(at::Tensor & out, at::TensorList tensors, at::Dimname dim) {
    return at::_ops::concat_names_out::call(tensors, dim, out);
}
// aten::concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & concat_outf(at::TensorList tensors, at::Dimname dim, at::Tensor & out) {
    return at::_ops::concat_names_out::call(tensors, dim, out);
}

// aten::concatenate(Tensor[] tensors, int dim=0) -> Tensor
inline at::Tensor concatenate(at::TensorList tensors, int64_t dim=0) {
    return at::_ops::concatenate::call(tensors, dim);
}

// aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & concatenate_out(at::Tensor & out, at::TensorList tensors, int64_t dim=0) {
    return at::_ops::concatenate_out::call(tensors, dim, out);
}
// aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & concatenate_outf(at::TensorList tensors, int64_t dim, at::Tensor & out) {
    return at::_ops::concatenate_out::call(tensors, dim, out);
}

// aten::concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor
inline at::Tensor concatenate(at::TensorList tensors, at::Dimname dim) {
    return at::_ops::concatenate_names::call(tensors, dim);
}

// aten::concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & concatenate_out(at::Tensor & out, at::TensorList tensors, at::Dimname dim) {
    return at::_ops::concatenate_names_out::call(tensors, dim, out);
}
// aten::concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & concatenate_outf(at::TensorList tensors, at::Dimname dim, at::Tensor & out) {
    return at::_ops::concatenate_names_out::call(tensors, dim, out);
}

// aten::block_diag(Tensor[] tensors) -> Tensor
inline at::Tensor block_diag(at::TensorList tensors) {
    return at::_ops::block_diag::call(tensors);
}

// aten::ceil(Tensor self) -> Tensor
inline at::Tensor ceil(const at::Tensor & self) {
    return at::_ops::ceil::call(self);
}

// aten::ceil_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & ceil_(at::Tensor & self) {
    return at::_ops::ceil_::call(self);
}

// aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ceil_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::ceil_out::call(self, out);
}
// aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ceil_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::ceil_out::call(self, out);
}

// aten::chain_matmul(Tensor[] matrices) -> Tensor
inline at::Tensor chain_matmul(at::TensorList matrices) {
    return at::_ops::chain_matmul::call(matrices);
}

// aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & chain_matmul_out(at::Tensor & out, at::TensorList matrices) {
    return at::_ops::chain_matmul_out::call(matrices, out);
}
// aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & chain_matmul_outf(at::TensorList matrices, at::Tensor & out) {
    return at::_ops::chain_matmul_out::call(matrices, out);
}

// aten::unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> unsafe_chunk(const at::Tensor & self, int64_t chunks, int64_t dim=0) {
    return at::_ops::unsafe_chunk::call(self, chunks, dim);
}

// aten::chunk(Tensor(a -> *) self, int chunks, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> chunk(const at::Tensor & self, int64_t chunks, int64_t dim=0) {
    return at::_ops::chunk::call(self, chunks, dim);
}

// aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, int64_t sections, int64_t dim=0) {
    return at::_ops::tensor_split_sections::call(self, sections, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, int64_t sections, int64_t dim=0) {
    return at::_ops::tensor_split_sections::call(self, sections, dim);
  }
}

// aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> tensor_split_symint(const at::Tensor & self, c10::SymInt sections, int64_t dim=0) {
    return at::_ops::tensor_split_sections::call(self, sections, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, c10::SymInt sections, int64_t dim=0) {
    return at::_ops::tensor_split_sections::call(self, sections, dim);
  }
}

// aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, at::IntArrayRef indices, int64_t dim=0) {
    return at::_ops::tensor_split_indices::call(self, c10::fromIntArrayRefSlow(indices), dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, at::IntArrayRef indices, int64_t dim=0) {
    return at::_ops::tensor_split_indices::call(self, c10::fromIntArrayRefSlow(indices), dim);
  }
}

// aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> tensor_split_symint(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim=0) {
    return at::_ops::tensor_split_indices::call(self, indices, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim=0) {
    return at::_ops::tensor_split_indices::call(self, indices, dim);
  }
}

// aten::tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim=0) {
    return at::_ops::tensor_split_tensor_indices_or_sections::call(self, tensor_indices_or_sections, dim);
}

// aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
inline at::Tensor clamp(const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max=::std::nullopt) {
    return at::_ops::clamp::call(self, min, max);
}

// aten::clamp.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor
inline at::Tensor clamp(const at::Tensor & self, const ::std::optional<at::Tensor> & min={}, const ::std::optional<at::Tensor> & max={}) {
    return at::_ops::clamp_Tensor::call(self, min, max);
}

// aten::clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)
inline at::Tensor & clamp_(at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max=::std::nullopt) {
    return at::_ops::clamp_::call(self, min, max);
}

// aten::clamp_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!)
inline at::Tensor & clamp_(at::Tensor & self, const ::std::optional<at::Tensor> & min={}, const ::std::optional<at::Tensor> & max={}) {
    return at::_ops::clamp__Tensor::call(self, min, max);
}

// aten::clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max=::std::nullopt) {
    return at::_ops::clamp_out::call(self, min, max, out);
}
// aten::clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max, at::Tensor & out) {
    return at::_ops::clamp_out::call(self, min, max, out);
}

// aten::clamp.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Tensor> & min={}, const ::std::optional<at::Tensor> & max={}) {
    return at::_ops::clamp_Tensor_out::call(self, min, max, out);
}
// aten::clamp.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_outf(const at::Tensor & self, const ::std::optional<at::Tensor> & min, const ::std::optional<at::Tensor> & max, at::Tensor & out) {
    return at::_ops::clamp_Tensor_out::call(self, min, max, out);
}

// aten::clamp_max(Tensor self, Scalar max) -> Tensor
inline at::Tensor clamp_max(const at::Tensor & self, const at::Scalar & max) {
    return at::_ops::clamp_max::call(self, max);
}

// aten::clamp_max.Tensor(Tensor self, Tensor max) -> Tensor
inline at::Tensor clamp_max(const at::Tensor & self, const at::Tensor & max) {
    return at::_ops::clamp_max_Tensor::call(self, max);
}

// aten::clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!)
inline at::Tensor & clamp_max_(at::Tensor & self, const at::Scalar & max) {
    return at::_ops::clamp_max_::call(self, max);
}

// aten::clamp_max_.Tensor(Tensor(a!) self, Tensor max) -> Tensor(a!)
inline at::Tensor & clamp_max_(at::Tensor & self, const at::Tensor & max) {
    return at::_ops::clamp_max__Tensor::call(self, max);
}

// aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_max_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & max) {
    return at::_ops::clamp_max_out::call(self, max, out);
}
// aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_max_outf(const at::Tensor & self, const at::Scalar & max, at::Tensor & out) {
    return at::_ops::clamp_max_out::call(self, max, out);
}

// aten::clamp_max.Tensor_out(Tensor self, Tensor max, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_max_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & max) {
    return at::_ops::clamp_max_Tensor_out::call(self, max, out);
}
// aten::clamp_max.Tensor_out(Tensor self, Tensor max, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_max_outf(const at::Tensor & self, const at::Tensor & max, at::Tensor & out) {
    return at::_ops::clamp_max_Tensor_out::call(self, max, out);
}

// aten::clamp_min(Tensor self, Scalar min) -> Tensor
inline at::Tensor clamp_min(const at::Tensor & self, const at::Scalar & min) {
    return at::_ops::clamp_min::call(self, min);
}

// aten::clamp_min.Tensor(Tensor self, Tensor min) -> Tensor
inline at::Tensor clamp_min(const at::Tensor & self, const at::Tensor & min) {
    return at::_ops::clamp_min_Tensor::call(self, min);
}

// aten::clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!)
inline at::Tensor & clamp_min_(at::Tensor & self, const at::Scalar & min) {
    return at::_ops::clamp_min_::call(self, min);
}

// aten::clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!)
inline at::Tensor & clamp_min_(at::Tensor & self, const at::Tensor & min) {
    return at::_ops::clamp_min__Tensor::call(self, min);
}

// aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_min_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & min) {
    return at::_ops::clamp_min_out::call(self, min, out);
}
// aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_min_outf(const at::Tensor & self, const at::Scalar & min, at::Tensor & out) {
    return at::_ops::clamp_min_out::call(self, min, out);
}

// aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_min_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & min) {
    return at::_ops::clamp_min_Tensor_out::call(self, min, out);
}
// aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clamp_min_outf(const at::Tensor & self, const at::Tensor & min, at::Tensor & out) {
    return at::_ops::clamp_min_Tensor_out::call(self, min, out);
}

// aten::clip(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
inline at::Tensor clip(const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max=::std::nullopt) {
    return at::_ops::clip::call(self, min, max);
}

// aten::clip.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor
inline at::Tensor clip(const at::Tensor & self, const ::std::optional<at::Tensor> & min={}, const ::std::optional<at::Tensor> & max={}) {
    return at::_ops::clip_Tensor::call(self, min, max);
}

// aten::clip_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!)
inline at::Tensor & clip_(at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max=::std::nullopt) {
    return at::_ops::clip_::call(self, min, max);
}

// aten::clip_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!)
inline at::Tensor & clip_(at::Tensor & self, const ::std::optional<at::Tensor> & min={}, const ::std::optional<at::Tensor> & max={}) {
    return at::_ops::clip__Tensor::call(self, min, max);
}

// aten::clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clip_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max=::std::nullopt) {
    return at::_ops::clip_out::call(self, min, max, out);
}
// aten::clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clip_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & min, const ::std::optional<at::Scalar> & max, at::Tensor & out) {
    return at::_ops::clip_out::call(self, min, max, out);
}

// aten::clip.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clip_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Tensor> & min={}, const ::std::optional<at::Tensor> & max={}) {
    return at::_ops::clip_Tensor_out::call(self, min, max, out);
}
// aten::clip.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clip_outf(const at::Tensor & self, const ::std::optional<at::Tensor> & min, const ::std::optional<at::Tensor> & max, at::Tensor & out) {
    return at::_ops::clip_Tensor_out::call(self, min, max, out);
}

// aten::cudnn_is_acceptable(Tensor self) -> bool
inline bool cudnn_is_acceptable(const at::Tensor & self) {
    return at::_ops::cudnn_is_acceptable::call(self);
}

// aten::complex(Tensor real, Tensor imag) -> Tensor
inline at::Tensor complex(const at::Tensor & real, const at::Tensor & imag) {
    return at::_ops::complex::call(real, imag);
}

// aten::complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & complex_out(at::Tensor & out, const at::Tensor & real, const at::Tensor & imag) {
    return at::_ops::complex_out::call(real, imag, out);
}
// aten::complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & complex_outf(const at::Tensor & real, const at::Tensor & imag, at::Tensor & out) {
    return at::_ops::complex_out::call(real, imag, out);
}

// aten::polar(Tensor abs, Tensor angle) -> Tensor
inline at::Tensor polar(const at::Tensor & abs, const at::Tensor & angle) {
    return at::_ops::polar::call(abs, angle);
}

// aten::polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & polar_out(at::Tensor & out, const at::Tensor & abs, const at::Tensor & angle) {
    return at::_ops::polar_out::call(abs, angle, out);
}
// aten::polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & polar_outf(const at::Tensor & abs, const at::Tensor & angle, at::Tensor & out) {
    return at::_ops::polar_out::call(abs, angle, out);
}

// aten::constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor
inline at::Tensor constant_pad_nd(const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value=0) {
    return at::_ops::constant_pad_nd::call(self, c10::fromIntArrayRefSlow(pad), value);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor constant_pad_nd(const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value=0) {
    return at::_ops::constant_pad_nd::call(self, c10::fromIntArrayRefSlow(pad), value);
  }
}

// aten::constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor
inline at::Tensor constant_pad_nd_symint(const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value=0) {
    return at::_ops::constant_pad_nd::call(self, pad, value);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor constant_pad_nd(const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value=0) {
    return at::_ops::constant_pad_nd::call(self, pad, value);
  }
}

// aten::convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor
inline at::Tensor convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) {
    return at::_ops::convolution::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) {
    return at::_ops::convolution::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups);
  }
}

// aten::convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor
inline at::Tensor convolution_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) {
    return at::_ops::convolution::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) {
    return at::_ops::convolution::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups);
  }
}

// aten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward::call(grad_output, input, weight, bias_sizes.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : ::std::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward::call(grad_output, input, weight, bias_sizes.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : ::std::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask);
  }
}

// aten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward_symint(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward::call(grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward::call(grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask);
  }
}

// aten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor
inline at::Tensor convolution_overrideable(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) {
    return at::_ops::convolution_overrideable::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor convolution_overrideable(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) {
    return at::_ops::convolution_overrideable::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups);
  }
}

// aten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor
inline at::Tensor convolution_overrideable_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) {
    return at::_ops::convolution_overrideable::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor convolution_overrideable(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) {
    return at::_ops::convolution_overrideable::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups);
  }
}

// aten::convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward_overrideable(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_overrideable::call(grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward_overrideable(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_overrideable::call(grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask);
  }
}

// aten::convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward_overrideable_symint(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_overrideable::call(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> convolution_backward_overrideable(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_overrideable::call(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask);
  }
}

// aten::_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor
inline at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) {
    return at::_ops::_convolution::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) {
    return at::_ops::_convolution::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32);
  }
}

// aten::_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor
inline at::Tensor _convolution_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) {
    return at::_ops::_convolution::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) {
    return at::_ops::_convolution::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32);
  }
}

// aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, int[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor
inline at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled) {
    return at::_ops::_convolution_deprecated::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled) {
    return at::_ops::_convolution_deprecated::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled);
  }
}

// aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, int[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor
inline at::Tensor _convolution_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) {
    return at::_ops::_convolution_deprecated::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) {
    return at::_ops::_convolution_deprecated::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled);
  }
}

// aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor _convolution_mode(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_convolution_mode::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _convolution_mode(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_convolution_mode::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor _convolution_mode_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_convolution_mode::call(input, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _convolution_mode(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_convolution_mode::call(input, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::_convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask);
  }
}

// aten::_convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward_symint(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _convolution_double_backward(const ::std::optional<at::Tensor> & ggI, const ::std::optional<at::Tensor> & ggW, const ::std::optional<at::Tensor> & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, output_mask);
  }
}

// aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv1d::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv1d::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv1d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv1d::call(input, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv1d::call(input, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv2d::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv2d::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv2d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv2d::call(input, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv2d::call(input, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv3d::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv3d::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv3d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv3d::call(input, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv3d::call(input, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, str padding="valid", SymInt[1] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv1d_padding::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv1d_padding::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, str padding="valid", SymInt[1] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv1d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv1d_padding::call(input, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv1d_padding::call(input, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, str padding="valid", SymInt[2] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv2d_padding::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv2d_padding::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, str padding="valid", SymInt[2] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv2d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv2d_padding::call(input, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv2d_padding::call(input, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, str padding="valid", SymInt[3] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv3d_padding::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) {
    return at::_ops::conv3d_padding::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, str padding="valid", SymInt[3] dilation=1, SymInt groups=1) -> Tensor
inline at::Tensor conv3d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv3d_padding::call(input, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) {
    return at::_ops::conv3d_padding::call(input, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor
inline at::Tensor conv_tbc(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad=0) {
    return at::_ops::conv_tbc::call(self, weight, bias, pad);
}

// aten::conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> conv_tbc_backward(const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) {
    return at::_ops::conv_tbc_backward::call(self, input, weight, bias, pad);
}

// aten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] output_padding=0, SymInt groups=1, SymInt[1] dilation=1) -> Tensor
inline at::Tensor conv_transpose1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) {
    return at::_ops::conv_transpose1d::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv_transpose1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) {
    return at::_ops::conv_transpose1d::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] output_padding=0, SymInt groups=1, SymInt[1] dilation=1) -> Tensor
inline at::Tensor conv_transpose1d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::conv_transpose1d::call(input, weight, bias, stride, padding, output_padding, groups, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv_transpose1d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::conv_transpose1d::call(input, weight, bias, stride, padding, output_padding, groups, dilation);
  }
}

// aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt groups=1, SymInt[2] dilation=1) -> Tensor
inline at::Tensor conv_transpose2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) {
    return at::_ops::conv_transpose2d_input::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv_transpose2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) {
    return at::_ops::conv_transpose2d_input::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt groups=1, SymInt[2] dilation=1) -> Tensor
inline at::Tensor conv_transpose2d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::conv_transpose2d_input::call(input, weight, bias, stride, padding, output_padding, groups, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv_transpose2d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::conv_transpose2d_input::call(input, weight, bias, stride, padding, output_padding, groups, dilation);
  }
}

// aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt groups=1, SymInt[3] dilation=1) -> Tensor
inline at::Tensor conv_transpose3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) {
    return at::_ops::conv_transpose3d_input::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv_transpose3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) {
    return at::_ops::conv_transpose3d_input::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt groups=1, SymInt[3] dilation=1) -> Tensor
inline at::Tensor conv_transpose3d_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::conv_transpose3d_input::call(input, weight, bias, stride, padding, output_padding, groups, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv_transpose3d(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::conv_transpose3d_input::call(input, weight, bias, stride, padding, output_padding, groups, dilation);
  }
}

// aten::copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor
inline at::Tensor copy(const at::Tensor & self, const at::Tensor & src, bool non_blocking=false) {
    return at::_ops::copy::call(self, src, non_blocking);
}

// aten::_copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor
inline at::Tensor _copy_from(const at::Tensor & self, const at::Tensor & dst, bool non_blocking=false) {
    return at::_ops::_copy_from::call(self, dst, non_blocking);
}

// aten::_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor
inline at::Tensor _copy_from_and_resize(const at::Tensor & self, const at::Tensor & dst) {
    return at::_ops::_copy_from_and_resize::call(self, dst);
}

// aten::cos(Tensor self) -> Tensor
inline at::Tensor cos(const at::Tensor & self) {
    return at::_ops::cos::call(self);
}

// aten::cos_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & cos_(at::Tensor & self) {
    return at::_ops::cos_::call(self);
}

// aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cos_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::cos_out::call(self, out);
}
// aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cos_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::cos_out::call(self, out);
}

// aten::cosh(Tensor self) -> Tensor
inline at::Tensor cosh(const at::Tensor & self) {
    return at::_ops::cosh::call(self);
}

// aten::cosh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & cosh_(at::Tensor & self) {
    return at::_ops::cosh_::call(self);
}

// aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cosh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::cosh_out::call(self, out);
}
// aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cosh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::cosh_out::call(self, out);
}

// aten::cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor
inline at::Tensor cosine_embedding_loss(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin=0.0, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::cosine_embedding_loss::call(input1, input2, target, margin, reduction);
}

// aten::count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor
inline at::Tensor count_nonzero(const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::count_nonzero_dim_IntList::call(self, dim);
}

// aten::count_nonzero(Tensor self, int? dim=None) -> Tensor
inline at::Tensor count_nonzero(const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::count_nonzero::call(self, dim);
}

// aten::cov(Tensor self, *, int correction=1, Tensor? fweights=None, Tensor? aweights=None) -> Tensor
inline at::Tensor cov(const at::Tensor & self, int64_t correction=1, const ::std::optional<at::Tensor> & fweights={}, const ::std::optional<at::Tensor> & aweights={}) {
    return at::_ops::cov::call(self, correction, fweights, aweights);
}

// aten::corrcoef(Tensor self) -> Tensor
inline at::Tensor corrcoef(const at::Tensor & self) {
    return at::_ops::corrcoef::call(self);
}

// aten::cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid
inline at::Tensor cudnn_affine_grid_generator(const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W) {
    return at::_ops::cudnn_affine_grid_generator::call(theta, N, C, H, W);
}

// aten::cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta
inline at::Tensor cudnn_affine_grid_generator_backward(const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) {
    return at::_ops::cudnn_affine_grid_generator_backward::call(grad, N, C, H, W);
}

// aten::cudnn_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> cudnn_batch_norm(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon) {
    return at::_ops::cudnn_batch_norm::call(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon);
}

// aten::cudnn_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> cudnn_batch_norm_backward(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon, const at::Tensor & reserveSpace) {
    return at::_ops::cudnn_batch_norm_backward::call(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace);
}

// aten::cudnn_convolution(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
inline at::Tensor cudnn_convolution(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor cudnn_convolution(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32);
  }
}

// aten::cudnn_convolution(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
inline at::Tensor cudnn_convolution_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution::call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor cudnn_convolution(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution::call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32);
  }
}

// aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & cudnn_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out);
  }
}

// aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) {
    return at::_ops::cudnn_convolution_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & cudnn_convolution_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) {
    return at::_ops::cudnn_convolution_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out);
  }
}

// aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_out::call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & cudnn_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_out::call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out);
  }
}

// aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) {
    return at::_ops::cudnn_convolution_out::call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & cudnn_convolution_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) {
    return at::_ops::cudnn_convolution_out::call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out);
  }
}

// aten::cudnn_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
inline at::Tensor cudnn_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_transpose::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor cudnn_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_transpose::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32);
  }
}

// aten::cudnn_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor
inline at::Tensor cudnn_convolution_transpose_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_transpose::call(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor cudnn_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_transpose::call(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32);
  }
}

// aten::_mps_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor _mps_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_mps_convolution_transpose::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _mps_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_mps_convolution_transpose::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::_mps_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor _mps_convolution_transpose_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_mps_convolution_transpose::call(self, weight, padding, output_padding, stride, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _mps_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_mps_convolution_transpose::call(self, weight, padding, output_padding, stride, dilation, groups);
  }
}

// aten::mps_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> mps_convolution_transpose_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,2> output_mask) {
    return at::_ops::mps_convolution_transpose_backward::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor> mps_convolution_transpose_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,2> output_mask) {
    return at::_ops::mps_convolution_transpose_backward::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask);
  }
}

// aten::mps_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> mps_convolution_transpose_backward_symint(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,2> output_mask) {
    return at::_ops::mps_convolution_transpose_backward::call(self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor> mps_convolution_transpose_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,2> output_mask) {
    return at::_ops::mps_convolution_transpose_backward::call(self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask);
  }
}

// aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor cudnn_convolution_relu(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::cudnn_convolution_relu::call(self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor cudnn_convolution_relu(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::cudnn_convolution_relu::call(self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor cudnn_convolution_relu_symint(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::cudnn_convolution_relu::call(self, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor cudnn_convolution_relu(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::cudnn_convolution_relu::call(self, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor cudnn_convolution_add_relu(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::cudnn_convolution_add_relu::call(self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor cudnn_convolution_add_relu(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::cudnn_convolution_add_relu::call(self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor cudnn_convolution_add_relu_symint(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::cudnn_convolution_add_relu::call(self, weight, z, alpha, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor cudnn_convolution_add_relu(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::cudnn_convolution_add_relu::call(self, weight, z, alpha, bias, stride, padding, dilation, groups);
  }
}

// aten::cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output
inline at::Tensor cudnn_grid_sampler(const at::Tensor & self, const at::Tensor & grid) {
    return at::_ops::cudnn_grid_sampler::call(self, grid);
}

// aten::cudnn_grid_sampler_backward(Tensor self, Tensor grid, Tensor grad_output) -> (Tensor grad_self, Tensor grad_grid)
inline ::std::tuple<at::Tensor,at::Tensor> cudnn_grid_sampler_backward(const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output) {
    return at::_ops::cudnn_grid_sampler_backward::call(self, grid, grad_output);
}

// aten::cummax(Tensor self, int dim) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> cummax(const at::Tensor & self, int64_t dim) {
    return at::_ops::cummax::call(self, dim);
}

// aten::cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> cummax_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim) {
    return at::_ops::cummax_out::call(self, dim, values, indices);
}
// aten::cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> cummax_outf(const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::cummax_out::call(self, dim, values, indices);
}

// aten::cummax.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> cummax(const at::Tensor & self, at::Dimname dim) {
    return at::_ops::cummax_dimname::call(self, dim);
}

// aten::cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> cummax_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim) {
    return at::_ops::cummax_dimname_out::call(self, dim, values, indices);
}
// aten::cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> cummax_outf(const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::cummax_dimname_out::call(self, dim, values, indices);
}

// aten::_cummax_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()
inline void _cummax_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) {
    return at::_ops::_cummax_helper::call(self, values, indices, dim);
}

// aten::cummin(Tensor self, int dim) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> cummin(const at::Tensor & self, int64_t dim) {
    return at::_ops::cummin::call(self, dim);
}

// aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> cummin_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim) {
    return at::_ops::cummin_out::call(self, dim, values, indices);
}
// aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> cummin_outf(const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::cummin_out::call(self, dim, values, indices);
}

// aten::cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> cummin(const at::Tensor & self, at::Dimname dim) {
    return at::_ops::cummin_dimname::call(self, dim);
}

// aten::cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> cummin_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim) {
    return at::_ops::cummin_dimname_out::call(self, dim, values, indices);
}
// aten::cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> cummin_outf(const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::cummin_dimname_out::call(self, dim, values, indices);
}

// aten::_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> ()
inline void _cummin_helper(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) {
    return at::_ops::_cummin_helper::call(self, values, indices, dim);
}

// aten::cummaxmin_backward(Tensor grad, Tensor input, Tensor indices, int dim) -> Tensor
inline at::Tensor cummaxmin_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & indices, int64_t dim) {
    return at::_ops::cummaxmin_backward::call(grad, input, indices, dim);
}

// aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor cumprod(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::cumprod::call(self, dim, dtype);
}

// aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cumprod_out(at::Tensor & out, const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::cumprod_out::call(self, dim, dtype, out);
}
// aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cumprod_outf(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::cumprod_out::call(self, dim, dtype, out);
}

// aten::cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor cumprod(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::cumprod_dimname::call(self, dim, dtype);
}

// aten::cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cumprod_out(at::Tensor & out, const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::cumprod_dimname_out::call(self, dim, dtype, out);
}
// aten::cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cumprod_outf(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::cumprod_dimname_out::call(self, dim, dtype, out);
}

// aten::cumprod_backward(Tensor grad, Tensor input, int dim, Tensor output) -> Tensor
inline at::Tensor cumprod_backward(const at::Tensor & grad, const at::Tensor & input, int64_t dim, const at::Tensor & output) {
    return at::_ops::cumprod_backward::call(grad, input, dim, output);
}

// aten::cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor cumsum(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::cumsum::call(self, dim, dtype);
}

// aten::cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cumsum_out(at::Tensor & out, const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::cumsum_out::call(self, dim, dtype, out);
}
// aten::cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cumsum_outf(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::cumsum_out::call(self, dim, dtype, out);
}

// aten::cumsum.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor cumsum(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::cumsum_dimname::call(self, dim, dtype);
}

// aten::cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cumsum_out(at::Tensor & out, const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::cumsum_dimname_out::call(self, dim, dtype, out);
}
// aten::cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cumsum_outf(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::cumsum_dimname_out::call(self, dim, dtype, out);
}

// aten::cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor
inline at::Tensor cumulative_trapezoid(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) {
    return at::_ops::cumulative_trapezoid_x::call(y, x, dim);
}

// aten::cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor
inline at::Tensor cumulative_trapezoid(const at::Tensor & y, const at::Scalar & dx=1, int64_t dim=-1) {
    return at::_ops::cumulative_trapezoid_dx::call(y, dx, dim);
}

// aten::ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor
inline at::Tensor ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=false) {
    return at::_ops::ctc_loss_IntList::call(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity);
}

// aten::ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor
inline at::Tensor ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=false) {
    return at::_ops::ctc_loss_Tensor::call(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity);
}

// aten::_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false) {
    return at::_ops::_ctc_loss::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity);
}

// aten::_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank=0, bool zero_infinity=false) {
    return at::_ops::_ctc_loss_Tensor::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity);
}

// aten::_ctc_loss_backward(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False) -> Tensor
inline at::Tensor _ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity=false) {
    return at::_ops::_ctc_loss_backward::call(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity);
}

// aten::_ctc_loss_backward.Tensor(Tensor grad, Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False) -> Tensor
inline at::Tensor _ctc_loss_backward(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity=false) {
    return at::_ops::_ctc_loss_backward_Tensor::call(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity);
}

// aten::diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor
inline at::Tensor diag_embed(const at::Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1) {
    return at::_ops::diag_embed::call(self, offset, dim1, dim2);
}

// aten::diagflat(Tensor self, int offset=0) -> Tensor
inline at::Tensor diagflat(const at::Tensor & self, int64_t offset=0) {
    return at::_ops::diagflat::call(self, offset);
}

// aten::diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a)
inline at::Tensor diagonal(const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) {
    return at::_ops::diagonal::call(self, offset, dim1, dim2);
}

// aten::linalg_diagonal(Tensor(a) A, *, int offset=0, int dim1=-2, int dim2=-1) -> Tensor(a)
inline at::Tensor linalg_diagonal(const at::Tensor & A, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1) {
    return at::_ops::linalg_diagonal::call(A, offset, dim1, dim2);
}

// aten::diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a)
inline at::Tensor diagonal(const at::Tensor & self, at::Dimname outdim, at::Dimname dim1, at::Dimname dim2, int64_t offset=0) {
    return at::_ops::diagonal_Dimname::call(self, outdim, dim1, dim2, offset);
}

// aten::diagonal_backward(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2) -> Tensor
inline at::Tensor diagonal_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) {
    return at::_ops::diagonal_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor diagonal_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) {
    return at::_ops::diagonal_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2);
  }
}

// aten::diagonal_backward(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2) -> Tensor
inline at::Tensor diagonal_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) {
    return at::_ops::diagonal_backward::call(grad_output, input_sizes, offset, dim1, dim2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor diagonal_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) {
    return at::_ops::diagonal_backward::call(grad_output, input_sizes, offset, dim1, dim2);
  }
}

// aten::diff(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None) -> Tensor
inline at::Tensor diff(const at::Tensor & self, int64_t n=1, int64_t dim=-1, const ::std::optional<at::Tensor> & prepend={}, const ::std::optional<at::Tensor> & append={}) {
    return at::_ops::diff::call(self, n, dim, prepend, append);
}

// aten::diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diff_out(at::Tensor & out, const at::Tensor & self, int64_t n=1, int64_t dim=-1, const ::std::optional<at::Tensor> & prepend={}, const ::std::optional<at::Tensor> & append={}) {
    return at::_ops::diff_out::call(self, n, dim, prepend, append, out);
}
// aten::diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diff_outf(const at::Tensor & self, int64_t n, int64_t dim, const ::std::optional<at::Tensor> & prepend, const ::std::optional<at::Tensor> & append, at::Tensor & out) {
    return at::_ops::diff_out::call(self, n, dim, prepend, append, out);
}

// aten::gradient.scalarint(Tensor self, *, Scalar? spacing=None, int? dim=None, int edge_order=1) -> Tensor[]
inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, const ::std::optional<at::Scalar> & spacing=::std::nullopt, ::std::optional<int64_t> dim=::std::nullopt, int64_t edge_order=1) {
    return at::_ops::gradient_scalarint::call(self, spacing, dim, edge_order);
}

// aten::gradient.scalararray(Tensor self, *, Scalar spacing, int[] dim, int edge_order=1) -> Tensor[]
inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, const at::Scalar & spacing, at::IntArrayRef dim, int64_t edge_order=1) {
    return at::_ops::gradient_scalararray::call(self, spacing, dim, edge_order);
}

// aten::gradient.array(Tensor self, *, int[] dim, int edge_order=1) -> Tensor[]
inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::IntArrayRef dim, int64_t edge_order=1) {
    return at::_ops::gradient_array::call(self, dim, edge_order);
}

// aten::gradient.scalarrayint(Tensor self, *, Scalar[] spacing, int? dim=None, int edge_order=1) -> Tensor[]
inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::ArrayRef<at::Scalar> spacing, ::std::optional<int64_t> dim=::std::nullopt, int64_t edge_order=1) {
    return at::_ops::gradient_scalarrayint::call(self, spacing, dim, edge_order);
}

// aten::gradient.scalarrayarray(Tensor self, *, Scalar[] spacing, int[] dim, int edge_order=1) -> Tensor[]
inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::ArrayRef<at::Scalar> spacing, at::IntArrayRef dim, int64_t edge_order=1) {
    return at::_ops::gradient_scalarrayarray::call(self, spacing, dim, edge_order);
}

// aten::gradient.tensorarrayint(Tensor self, *, Tensor[] spacing, int? dim=None, int edge_order=1) -> Tensor[]
inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::TensorList spacing, ::std::optional<int64_t> dim=::std::nullopt, int64_t edge_order=1) {
    return at::_ops::gradient_tensorarrayint::call(self, spacing, dim, edge_order);
}

// aten::gradient.tensorarray(Tensor self, *, Tensor[] spacing, int[] dim, int edge_order=1) -> Tensor[]
inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::TensorList spacing, at::IntArrayRef dim, int64_t edge_order=1) {
    return at::_ops::gradient_tensorarray::call(self, spacing, dim, edge_order);
}

// aten::div.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor div(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::div_Tensor::call(self, other);
}

// aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & div_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::div_out::call(self, other, out);
}
// aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & div_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::div_out::call(self, other, out);
}

// aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor
inline at::Tensor div(const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode) {
    return at::_ops::div_Tensor_mode::call(self, other, rounding_mode);
}

// aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & div_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode) {
    return at::_ops::div_out_mode::call(self, other, rounding_mode, out);
}
// aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & div_outf(const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode, at::Tensor & out) {
    return at::_ops::div_out_mode::call(self, other, rounding_mode, out);
}

// aten::div.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor div(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::div_Scalar::call(self, other);
}

// aten::div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor
inline at::Tensor div(const at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode) {
    return at::_ops::div_Scalar_mode::call(self, other, rounding_mode);
}

// aten::divide.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor divide(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::divide_Tensor::call(self, other);
}

// aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & divide_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::divide_out::call(self, other, out);
}
// aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & divide_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::divide_out::call(self, other, out);
}

// aten::divide.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor divide(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::divide_Scalar::call(self, other);
}

// aten::divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor
inline at::Tensor divide(const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode) {
    return at::_ops::divide_Tensor_mode::call(self, other, rounding_mode);
}

// aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & divide_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode) {
    return at::_ops::divide_out_mode::call(self, other, rounding_mode, out);
}
// aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & divide_outf(const at::Tensor & self, const at::Tensor & other, ::std::optional<c10::string_view> rounding_mode, at::Tensor & out) {
    return at::_ops::divide_out_mode::call(self, other, rounding_mode, out);
}

// aten::divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor
inline at::Tensor divide(const at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode) {
    return at::_ops::divide_Scalar_mode::call(self, other, rounding_mode);
}

// aten::true_divide.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor true_divide(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::true_divide_Tensor::call(self, other);
}

// aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & true_divide_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::true_divide_out::call(self, other, out);
}
// aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & true_divide_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::true_divide_out::call(self, other, out);
}

// aten::true_divide.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor true_divide(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::true_divide_Scalar::call(self, other);
}

// aten::dot(Tensor self, Tensor tensor) -> Tensor
inline at::Tensor dot(const at::Tensor & self, const at::Tensor & tensor) {
    return at::_ops::dot::call(self, tensor);
}

// aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & dot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor) {
    return at::_ops::dot_out::call(self, tensor, out);
}
// aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & dot_outf(const at::Tensor & self, const at::Tensor & tensor, at::Tensor & out) {
    return at::_ops::dot_out::call(self, tensor, out);
}

// aten::vdot(Tensor self, Tensor other) -> Tensor
inline at::Tensor vdot(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::vdot::call(self, other);
}

// aten::vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & vdot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::vdot_out::call(self, other, out);
}
// aten::vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & vdot_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::vdot_out::call(self, other, out);
}

// aten::einsum(str equation, Tensor[] tensors, *, int[]? path=None) -> Tensor
inline at::Tensor einsum(c10::string_view equation, at::TensorList tensors, at::OptionalIntArrayRef path=::std::nullopt) {
    return at::_ops::einsum::call(equation, tensors, path);
}

// aten::embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor
inline at::Tensor embedding(const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) {
    return at::_ops::embedding::call(weight, indices, padding_idx, scale_grad_by_freq, sparse);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor embedding(const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) {
    return at::_ops::embedding::call(weight, indices, padding_idx, scale_grad_by_freq, sparse);
  }
}

// aten::embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor
inline at::Tensor embedding_symint(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) {
    return at::_ops::embedding::call(weight, indices, padding_idx, scale_grad_by_freq, sparse);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor embedding(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) {
    return at::_ops::embedding::call(weight, indices, padding_idx, scale_grad_by_freq, sparse);
  }
}

// aten::embedding_backward(Tensor grad, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor
inline at::Tensor embedding_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) {
    return at::_ops::embedding_backward::call(grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor embedding_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) {
    return at::_ops::embedding_backward::call(grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse);
  }
}

// aten::embedding_backward(Tensor grad, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor
inline at::Tensor embedding_backward_symint(const at::Tensor & grad, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse) {
    return at::_ops::embedding_backward::call(grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor embedding_backward(const at::Tensor & grad, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse) {
    return at::_ops::embedding_backward::call(grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse);
  }
}

// aten::embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor
inline at::Tensor embedding_dense_backward(const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_dense_backward::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor embedding_dense_backward(const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_dense_backward::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq);
  }
}

// aten::embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor
inline at::Tensor embedding_dense_backward_symint(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_dense_backward::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor embedding_dense_backward(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_dense_backward::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq);
  }
}

// aten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!)
inline at::Tensor & embedding_renorm_(at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) {
    return at::_ops::embedding_renorm_::call(self, indices, max_norm, norm_type);
}

// aten::embedding_sparse_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq) -> Tensor
inline at::Tensor embedding_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_sparse_backward::call(grad, indices, num_weights, padding_idx, scale_grad_by_freq);
}

// aten::_embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag_forward_only(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const ::std::optional<at::Tensor> & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_forward_only::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx);
}

// aten::_rowwise_prune(Tensor weight, Tensor mask, ScalarType compressed_indices_dtype) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _rowwise_prune(const at::Tensor & weight, const at::Tensor & mask, at::ScalarType compressed_indices_dtype) {
    return at::_ops::_rowwise_prune::call(weight, mask, compressed_indices_dtype);
}

// aten::row_stack(Tensor[] tensors) -> Tensor
inline at::Tensor row_stack(at::TensorList tensors) {
    return at::_ops::row_stack::call(tensors);
}

// aten::row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & row_stack_out(at::Tensor & out, at::TensorList tensors) {
    return at::_ops::row_stack_out::call(tensors, out);
}
// aten::row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & row_stack_outf(at::TensorList tensors, at::Tensor & out) {
    return at::_ops::row_stack_out::call(tensors, out);
}

// aten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const ::std::optional<at::Tensor> & per_sample_weights={}, bool include_last_offset=false) {
    return at::_ops::embedding_bag::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset);
}

// aten::embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, bool include_last_offset, ::std::optional<int64_t> padding_idx) {
    return at::_ops::embedding_bag_padding_idx::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx);
}

// aten::_embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _embedding_bag(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const ::std::optional<at::Tensor> & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx);
}

// aten::_embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
inline at::Tensor _embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx);
  }
}

// aten::_embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
inline at::Tensor _embedding_bag_backward_symint(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _embedding_bag_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx);
  }
}

// aten::_embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
inline at::Tensor _embedding_bag_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_sparse_backward::call(grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _embedding_bag_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_sparse_backward::call(grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx);
  }
}

// aten::_embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
inline at::Tensor _embedding_bag_sparse_backward_symint(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_sparse_backward::call(grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _embedding_bag_sparse_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_sparse_backward::call(grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx);
  }
}

// aten::_embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
inline at::Tensor _embedding_bag_dense_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_dense_backward::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _embedding_bag_dense_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_dense_backward::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx);
  }
}

// aten::_embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor
inline at::Tensor _embedding_bag_dense_backward_symint(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_dense_backward::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _embedding_bag_dense_backward(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_dense_backward::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx);
  }
}

// aten::_embedding_bag_per_sample_weights_backward(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1) -> Tensor
inline at::Tensor _embedding_bag_per_sample_weights_backward(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_per_sample_weights_backward::call(grad, weight, indices, offsets, offset2bag, mode, padding_idx);
}

// aten::empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::empty_names::call(size, names, dtype, layout, device, pin_memory, memory_format);
}

// aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty(at::IntArrayRef size, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_memory_format::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor empty(at::IntArrayRef size, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_memory_format::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::empty_memory_format::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor empty(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::empty_memory_format::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, memory_format);
  }
}

// aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty_symint(c10::SymIntArrayRef size, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_memory_format::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor empty(c10::SymIntArrayRef size, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_memory_format::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::empty_memory_format::call(size, dtype, layout, device, pin_memory, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor empty(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::empty_memory_format::call(size, dtype, layout, device, pin_memory, memory_format);
  }
}

// aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor empty_permuted(at::IntArrayRef size, at::IntArrayRef physical_layout, at::TensorOptions options={}) {
    return at::_ops::empty_permuted::call(c10::fromIntArrayRefSlow(size), physical_layout, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor empty_permuted(at::IntArrayRef size, at::IntArrayRef physical_layout, at::TensorOptions options={}) {
    return at::_ops::empty_permuted::call(c10::fromIntArrayRefSlow(size), physical_layout, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor empty_permuted(at::IntArrayRef size, at::IntArrayRef physical_layout, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::empty_permuted::call(c10::fromIntArrayRefSlow(size), physical_layout, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor empty_permuted(at::IntArrayRef size, at::IntArrayRef physical_layout, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::empty_permuted::call(c10::fromIntArrayRefSlow(size), physical_layout, dtype, layout, device, pin_memory);
  }
}

// aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor empty_permuted_symint(c10::SymIntArrayRef size, at::IntArrayRef physical_layout, at::TensorOptions options={}) {
    return at::_ops::empty_permuted::call(size, physical_layout, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor empty_permuted(c10::SymIntArrayRef size, at::IntArrayRef physical_layout, at::TensorOptions options={}) {
    return at::_ops::empty_permuted::call(size, physical_layout, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor empty_permuted_symint(c10::SymIntArrayRef size, at::IntArrayRef physical_layout, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::empty_permuted::call(size, physical_layout, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor empty_permuted(c10::SymIntArrayRef size, at::IntArrayRef physical_layout, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::empty_permuted::call(size, physical_layout, dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_empty(const at::Tensor & self, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::new_empty::call(self, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_empty(const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_empty::call(self, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_empty(const at::Tensor & self, c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::new_empty::call(self, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_empty(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_empty::call(self, size, dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_empty_strided(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::TensorOptions options={}) {
    return at::_ops::new_empty_strided::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_empty_strided(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_empty_strided::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_empty_strided(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::TensorOptions options={}) {
    return at::_ops::new_empty_strided::call(self, size, stride, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_empty_strided(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_empty_strided::call(self, size, stride, dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_full(const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) {
    return at::_ops::new_full::call(self, c10::fromIntArrayRefSlow(size), fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_full(const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_full::call(self, c10::fromIntArrayRefSlow(size), fill_value, dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_full(const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) {
    return at::_ops::new_full::call(self, size, fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_full(const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_full::call(self, size, fill_value, dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_zeros(const at::Tensor & self, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::new_zeros::call(self, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_zeros(const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_zeros::call(self, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_zeros(const at::Tensor & self, c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::new_zeros::call(self, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_zeros(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_zeros::call(self, size, dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_ones(const at::Tensor & self, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::new_ones::call(self, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor new_ones(const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_ones::call(self, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_ones(const at::Tensor & self, c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::new_ones::call(self, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor new_ones(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::new_ones::call(self, size, dtype, layout, device, pin_memory);
  }
}

// aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor
inline at::Tensor _empty_affine_quantized(at::IntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_affine_quantized::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _empty_affine_quantized(at::IntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_affine_quantized::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor
inline at::Tensor _empty_affine_quantized(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_empty_affine_quantized::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, scale, zero_point, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _empty_affine_quantized(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_empty_affine_quantized::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, scale, zero_point, memory_format);
  }
}

// aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor
inline at::Tensor _empty_affine_quantized_symint(c10::SymIntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_affine_quantized::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _empty_affine_quantized(c10::SymIntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_affine_quantized::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor
inline at::Tensor _empty_affine_quantized_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_empty_affine_quantized::call(size, dtype, layout, device, pin_memory, scale, zero_point, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _empty_affine_quantized(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_empty_affine_quantized::call(size, dtype, layout, device, pin_memory, scale, zero_point, memory_format);
  }
}

// aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor
inline at::Tensor _empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_per_channel_affine_quantized::call(c10::fromIntArrayRefSlow(size), scales, zero_points, axis, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_per_channel_affine_quantized::call(c10::fromIntArrayRefSlow(size), scales, zero_points, axis, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor
inline at::Tensor _empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_empty_per_channel_affine_quantized::call(c10::fromIntArrayRefSlow(size), scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_empty_per_channel_affine_quantized::call(c10::fromIntArrayRefSlow(size), scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format);
  }
}

// aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor
inline at::Tensor _empty_per_channel_affine_quantized_symint(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_per_channel_affine_quantized::call(size, scales, zero_points, axis, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _empty_per_channel_affine_quantized(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_per_channel_affine_quantized::call(size, scales, zero_points, axis, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor
inline at::Tensor _empty_per_channel_affine_quantized_symint(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_empty_per_channel_affine_quantized::call(size, scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _empty_per_channel_affine_quantized(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_empty_per_channel_affine_quantized::call(size, scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & resize_(const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_::call(self, c10::fromIntArrayRefSlow(size), memory_format);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & resize_(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_::call(self, size, memory_format);
  }
}

// aten::_resize_output_(Tensor(a!) self, SymInt[] size, Device device) -> Tensor(a!)
inline const at::Tensor & _resize_output_(const at::Tensor & self, at::IntArrayRef size, at::Device device) {
    return at::_ops::_resize_output_::call(self, c10::fromIntArrayRefSlow(size), device);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & _resize_output_(const at::Tensor & self, at::IntArrayRef size, at::Device device) {
    return at::_ops::_resize_output_::call(self, c10::fromIntArrayRefSlow(size), device);
  }
}

// aten::_resize_output_(Tensor(a!) self, SymInt[] size, Device device) -> Tensor(a!)
inline const at::Tensor & _resize_output__symint(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) {
    return at::_ops::_resize_output_::call(self, size, device);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & _resize_output_(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) {
    return at::_ops::_resize_output_::call(self, size, device);
  }
}

// aten::empty_quantized(int[] size, Tensor qtensor, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty_quantized(at::IntArrayRef size, const at::Tensor & qtensor, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_quantized::call(size, qtensor, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::empty_quantized(int[] size, Tensor qtensor, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty_quantized(at::IntArrayRef size, const at::Tensor & qtensor, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::empty_quantized::call(size, qtensor, dtype, layout, device, pin_memory, memory_format);
}

// aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_out::call(c10::fromIntArrayRefSlow(size), memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & empty_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_out::call(c10::fromIntArrayRefSlow(size), memory_format, out);
  }
}

// aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_outf(at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::empty_out::call(c10::fromIntArrayRefSlow(size), memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & empty_outf(at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::empty_out::call(c10::fromIntArrayRefSlow(size), memory_format, out);
  }
}

// aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_out::call(size, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & empty_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_out::call(size, memory_format, out);
  }
}

// aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::empty_out::call(size, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & empty_outf(c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::empty_out::call(size, memory_format, out);
  }
}

// aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor empty_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::empty_like::call(self, dtype, layout, device, pin_memory, memory_format);
}

// aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor empty_strided(at::IntArrayRef size, at::IntArrayRef stride, at::TensorOptions options={}) {
    return at::_ops::empty_strided::call(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor empty_strided(at::IntArrayRef size, at::IntArrayRef stride, at::TensorOptions options={}) {
    return at::_ops::empty_strided::call(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor empty_strided(at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::empty_strided::call(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor empty_strided(at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::empty_strided::call(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), dtype, layout, device, pin_memory);
  }
}

// aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor empty_strided_symint(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::TensorOptions options={}) {
    return at::_ops::empty_strided::call(size, stride, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor empty_strided(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::TensorOptions options={}) {
    return at::_ops::empty_strided::call(size, stride, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor empty_strided_symint(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::empty_strided::call(size, stride, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor empty_strided(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::empty_strided::call(size, stride, dtype, layout, device, pin_memory);
  }
}

// aten::erf(Tensor self) -> Tensor
inline at::Tensor erf(const at::Tensor & self) {
    return at::_ops::erf::call(self);
}

// aten::erf_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & erf_(at::Tensor & self) {
    return at::_ops::erf_::call(self);
}

// aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & erf_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::erf_out::call(self, out);
}
// aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & erf_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::erf_out::call(self, out);
}

// aten::erfc(Tensor self) -> Tensor
inline at::Tensor erfc(const at::Tensor & self) {
    return at::_ops::erfc::call(self);
}

// aten::erfc_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & erfc_(at::Tensor & self) {
    return at::_ops::erfc_::call(self);
}

// aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & erfc_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::erfc_out::call(self, out);
}
// aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & erfc_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::erfc_out::call(self, out);
}

// aten::exp(Tensor self) -> Tensor
inline at::Tensor exp(const at::Tensor & self) {
    return at::_ops::exp::call(self);
}

// aten::exp_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & exp_(at::Tensor & self) {
    return at::_ops::exp_::call(self);
}

// aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & exp_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::exp_out::call(self, out);
}
// aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & exp_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::exp_out::call(self, out);
}

// aten::exp2(Tensor self) -> Tensor
inline at::Tensor exp2(const at::Tensor & self) {
    return at::_ops::exp2::call(self);
}

// aten::exp2_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & exp2_(at::Tensor & self) {
    return at::_ops::exp2_::call(self);
}

// aten::exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & exp2_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::exp2_out::call(self, out);
}
// aten::exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & exp2_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::exp2_out::call(self, out);
}

// aten::expm1(Tensor self) -> Tensor
inline at::Tensor expm1(const at::Tensor & self) {
    return at::_ops::expm1::call(self);
}

// aten::expm1_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & expm1_(at::Tensor & self) {
    return at::_ops::expm1_::call(self);
}

// aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & expm1_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::expm1_out::call(self, out);
}
// aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & expm1_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::expm1_out::call(self, out);
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor expand(const at::Tensor & self, at::IntArrayRef size, bool implicit=false) {
    return at::_ops::expand::call(self, c10::fromIntArrayRefSlow(size), implicit);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor expand(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit=false) {
    return at::_ops::expand::call(self, size, implicit);
  }
}

// aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor eye(int64_t n, at::TensorOptions options={}) {
    return at::_ops::eye::call(n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor eye(int64_t n, at::TensorOptions options={}) {
    return at::_ops::eye::call(n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor eye(int64_t n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::eye::call(n, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor eye(int64_t n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::eye::call(n, dtype, layout, device, pin_memory);
  }
}

// aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor eye_symint(c10::SymInt n, at::TensorOptions options={}) {
    return at::_ops::eye::call(n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor eye(c10::SymInt n, at::TensorOptions options={}) {
    return at::_ops::eye::call(n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor eye_symint(c10::SymInt n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::eye::call(n, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor eye(c10::SymInt n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::eye::call(n, dtype, layout, device, pin_memory);
  }
}

// aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor eye(int64_t n, int64_t m, at::TensorOptions options={}) {
    return at::_ops::eye_m::call(n, m, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor eye(int64_t n, int64_t m, at::TensorOptions options={}) {
    return at::_ops::eye_m::call(n, m, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor eye(int64_t n, int64_t m, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::eye_m::call(n, m, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor eye(int64_t n, int64_t m, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::eye_m::call(n, m, dtype, layout, device, pin_memory);
  }
}

// aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor eye_symint(c10::SymInt n, c10::SymInt m, at::TensorOptions options={}) {
    return at::_ops::eye_m::call(n, m, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor eye(c10::SymInt n, c10::SymInt m, at::TensorOptions options={}) {
    return at::_ops::eye_m::call(n, m, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor eye_symint(c10::SymInt n, c10::SymInt m, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::eye_m::call(n, m, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor eye(c10::SymInt n, c10::SymInt m, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::eye_m::call(n, m, dtype, layout, device, pin_memory);
  }
}

// aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eye_out(at::Tensor & out, int64_t n) {
    return at::_ops::eye_out::call(n, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & eye_out(at::Tensor & out, int64_t n) {
    return at::_ops::eye_out::call(n, out);
  }
}

// aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eye_outf(int64_t n, at::Tensor & out) {
    return at::_ops::eye_out::call(n, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & eye_outf(int64_t n, at::Tensor & out) {
    return at::_ops::eye_out::call(n, out);
  }
}

// aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eye_symint_out(at::Tensor & out, c10::SymInt n) {
    return at::_ops::eye_out::call(n, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & eye_out(at::Tensor & out, c10::SymInt n) {
    return at::_ops::eye_out::call(n, out);
  }
}

// aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eye_symint_outf(c10::SymInt n, at::Tensor & out) {
    return at::_ops::eye_out::call(n, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & eye_outf(c10::SymInt n, at::Tensor & out) {
    return at::_ops::eye_out::call(n, out);
  }
}

// aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eye_out(at::Tensor & out, int64_t n, int64_t m) {
    return at::_ops::eye_m_out::call(n, m, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & eye_out(at::Tensor & out, int64_t n, int64_t m) {
    return at::_ops::eye_m_out::call(n, m, out);
  }
}

// aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eye_outf(int64_t n, int64_t m, at::Tensor & out) {
    return at::_ops::eye_m_out::call(n, m, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & eye_outf(int64_t n, int64_t m, at::Tensor & out) {
    return at::_ops::eye_m_out::call(n, m, out);
  }
}

// aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eye_symint_out(at::Tensor & out, c10::SymInt n, c10::SymInt m) {
    return at::_ops::eye_m_out::call(n, m, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & eye_out(at::Tensor & out, c10::SymInt n, c10::SymInt m) {
    return at::_ops::eye_m_out::call(n, m, out);
  }
}

// aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eye_symint_outf(c10::SymInt n, c10::SymInt m, at::Tensor & out) {
    return at::_ops::eye_m_out::call(n, m, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & eye_outf(c10::SymInt n, c10::SymInt m, at::Tensor & out) {
    return at::_ops::eye_m_out::call(n, m, out);
  }
}

// aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)
inline at::Tensor flatten(const at::Tensor & self, int64_t start_dim=0, int64_t end_dim=-1) {
    return at::_ops::flatten_using_ints::call(self, start_dim, end_dim);
}

// aten::flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a)
inline at::Tensor flatten(const at::Tensor & self, int64_t start_dim, int64_t end_dim, at::Dimname out_dim) {
    return at::_ops::flatten_named_out_dim::call(self, start_dim, end_dim, out_dim);
}

// aten::flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a)
inline at::Tensor flatten(const at::Tensor & self, at::Dimname start_dim, at::Dimname end_dim, at::Dimname out_dim) {
    return at::_ops::flatten_using_names::call(self, start_dim, end_dim, out_dim);
}

// aten::flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a)
inline at::Tensor flatten(const at::Tensor & self, at::DimnameList dims, at::Dimname out_dim) {
    return at::_ops::flatten_DimnameList::call(self, dims, out_dim);
}

// aten::unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a)
inline at::Tensor unflatten(const at::Tensor & self, int64_t dim, at::IntArrayRef sizes) {
    return at::_ops::unflatten_int::call(self, dim, c10::fromIntArrayRefSlow(sizes));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor unflatten(const at::Tensor & self, int64_t dim, at::IntArrayRef sizes) {
    return at::_ops::unflatten_int::call(self, dim, c10::fromIntArrayRefSlow(sizes));
  }
}

// aten::unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a)
inline at::Tensor unflatten_symint(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes) {
    return at::_ops::unflatten_int::call(self, dim, sizes);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor unflatten(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes) {
    return at::_ops::unflatten_int::call(self, dim, sizes);
  }
}

// aten::unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a)
inline at::Tensor unflatten(const at::Tensor & self, at::Dimname dim, at::IntArrayRef sizes, at::DimnameList names) {
    return at::_ops::unflatten_Dimname::call(self, dim, c10::fromIntArrayRefSlow(sizes), names);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor unflatten(const at::Tensor & self, at::Dimname dim, at::IntArrayRef sizes, at::DimnameList names) {
    return at::_ops::unflatten_Dimname::call(self, dim, c10::fromIntArrayRefSlow(sizes), names);
  }
}

// aten::unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a)
inline at::Tensor unflatten_symint(const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names) {
    return at::_ops::unflatten_Dimname::call(self, dim, sizes, names);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor unflatten(const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names) {
    return at::_ops::unflatten_Dimname::call(self, dim, sizes, names);
  }
}

// aten::fill.Scalar(Tensor self, Scalar value) -> Tensor
inline at::Tensor fill(const at::Tensor & self, const at::Scalar & value) {
    return at::_ops::fill_Scalar::call(self, value);
}

// aten::fill.Tensor(Tensor self, Tensor value) -> Tensor
inline at::Tensor fill(const at::Tensor & self, const at::Tensor & value) {
    return at::_ops::fill_Tensor::call(self, value);
}

// aten::fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!)
inline at::Tensor & fill_(at::Tensor & self, const at::Scalar & value) {
    return at::_ops::fill__Scalar::call(self, value);
}

// aten::fill_.Tensor(Tensor(a!) self, Tensor value) -> Tensor(a!)
inline at::Tensor & fill_(at::Tensor & self, const at::Tensor & value) {
    return at::_ops::fill__Tensor::call(self, value);
}

// aten::floor(Tensor self) -> Tensor
inline at::Tensor floor(const at::Tensor & self) {
    return at::_ops::floor::call(self);
}

// aten::floor_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & floor_(at::Tensor & self) {
    return at::_ops::floor_::call(self);
}

// aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & floor_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::floor_out::call(self, out);
}
// aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & floor_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::floor_out::call(self, out);
}

// aten::floor_divide(Tensor self, Tensor other) -> Tensor
inline at::Tensor floor_divide(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::floor_divide::call(self, other);
}

// aten::floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & floor_divide_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::floor_divide_out::call(self, other, out);
}
// aten::floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & floor_divide_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::floor_divide_out::call(self, other, out);
}

// aten::floor_divide.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor floor_divide(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::floor_divide_Scalar::call(self, other);
}

// aten::frac(Tensor self) -> Tensor
inline at::Tensor frac(const at::Tensor & self) {
    return at::_ops::frac::call(self);
}

// aten::frac_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & frac_(at::Tensor & self) {
    return at::_ops::frac_::call(self);
}

// aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & frac_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::frac_out::call(self, out);
}
// aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & frac_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::frac_out::call(self, out);
}

// aten::full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor full(at::IntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::full_names::call(size, fill_value, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor full(at::IntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::full_names::call(size, fill_value, names, dtype, layout, device, pin_memory);
}

// aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor full(at::IntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) {
    return at::_ops::full::call(c10::fromIntArrayRefSlow(size), fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor full(at::IntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) {
    return at::_ops::full::call(c10::fromIntArrayRefSlow(size), fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor full(at::IntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::full::call(c10::fromIntArrayRefSlow(size), fill_value, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor full(at::IntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::full::call(c10::fromIntArrayRefSlow(size), fill_value, dtype, layout, device, pin_memory);
  }
}

// aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor full_symint(c10::SymIntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) {
    return at::_ops::full::call(size, fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor full(c10::SymIntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) {
    return at::_ops::full::call(size, fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor full_symint(c10::SymIntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::full::call(size, fill_value, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor full(c10::SymIntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::full::call(size, fill_value, dtype, layout, device, pin_memory);
  }
}

// aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & full_out(at::Tensor & out, at::IntArrayRef size, const at::Scalar & fill_value) {
    return at::_ops::full_out::call(c10::fromIntArrayRefSlow(size), fill_value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & full_out(at::Tensor & out, at::IntArrayRef size, const at::Scalar & fill_value) {
    return at::_ops::full_out::call(c10::fromIntArrayRefSlow(size), fill_value, out);
  }
}

// aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & full_outf(at::IntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) {
    return at::_ops::full_out::call(c10::fromIntArrayRefSlow(size), fill_value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & full_outf(at::IntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) {
    return at::_ops::full_out::call(c10::fromIntArrayRefSlow(size), fill_value, out);
  }
}

// aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & full_symint_out(at::Tensor & out, c10::SymIntArrayRef size, const at::Scalar & fill_value) {
    return at::_ops::full_out::call(size, fill_value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & full_out(at::Tensor & out, c10::SymIntArrayRef size, const at::Scalar & fill_value) {
    return at::_ops::full_out::call(size, fill_value, out);
  }
}

// aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & full_symint_outf(c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) {
    return at::_ops::full_out::call(size, fill_value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & full_outf(c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) {
    return at::_ops::full_out::call(size, fill_value, out);
  }
}

// aten::full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor full_like(const at::Tensor & self, const at::Scalar & fill_value, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::full_like::call(self, fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor full_like(const at::Tensor & self, const at::Scalar & fill_value, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::full_like::call(self, fill_value, dtype, layout, device, pin_memory, memory_format);
}

// aten::from_file(str filename, bool? shared=None, int? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor from_file(c10::string_view filename, ::std::optional<bool> shared=::std::nullopt, ::std::optional<int64_t> size=0, at::TensorOptions options={}) {
    return at::_ops::from_file::call(filename, shared, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::from_file(str filename, bool? shared=None, int? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor from_file(c10::string_view filename, ::std::optional<bool> shared, ::std::optional<int64_t> size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::from_file::call(filename, shared, size, dtype, layout, device, pin_memory);
}

// aten::gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gcd_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::gcd_out::call(self, other, out);
}
// aten::gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gcd_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::gcd_out::call(self, other, out);
}

// aten::gcd(Tensor self, Tensor other) -> Tensor
inline at::Tensor gcd(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::gcd::call(self, other);
}

// aten::gcd_(Tensor(a!) self, Tensor other) -> Tensor(a!)
inline at::Tensor & gcd_(at::Tensor & self, const at::Tensor & other) {
    return at::_ops::gcd_::call(self, other);
}

// aten::lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lcm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::lcm_out::call(self, other, out);
}
// aten::lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lcm_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::lcm_out::call(self, other, out);
}

// aten::lcm(Tensor self, Tensor other) -> Tensor
inline at::Tensor lcm(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::lcm::call(self, other);
}

// aten::lcm_(Tensor(a!) self, Tensor other) -> Tensor(a!)
inline at::Tensor & lcm_(at::Tensor & self, const at::Tensor & other) {
    return at::_ops::lcm_::call(self, other);
}

// aten::grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
inline at::Tensor grid_sampler(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
    return at::_ops::grid_sampler::call(input, grid, interpolation_mode, padding_mode, align_corners);
}

// aten::grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
inline at::Tensor grid_sampler_2d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
    return at::_ops::grid_sampler_2d::call(input, grid, interpolation_mode, padding_mode, align_corners);
}

// aten::grid_sampler_2d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> grid_sampler_2d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) {
    return at::_ops::grid_sampler_2d_backward::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask);
}

// aten::_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
inline at::Tensor _grid_sampler_2d_cpu_fallback(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
    return at::_ops::_grid_sampler_2d_cpu_fallback::call(input, grid, interpolation_mode, padding_mode, align_corners);
}

// aten::_grid_sampler_2d_cpu_fallback_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _grid_sampler_2d_cpu_fallback_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
    return at::_ops::_grid_sampler_2d_cpu_fallback_backward::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners);
}

// aten::grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor
inline at::Tensor grid_sampler_3d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
    return at::_ops::grid_sampler_3d::call(input, grid, interpolation_mode, padding_mode, align_corners);
}

// aten::grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> grid_sampler_3d_backward(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) {
    return at::_ops::grid_sampler_3d_backward::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask);
}

// aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hann_window(int64_t window_length, at::TensorOptions options={}) {
    return at::_ops::hann_window::call(window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hann_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::hann_window::call(window_length, dtype, layout, device, pin_memory);
}

// aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hann_window(int64_t window_length, bool periodic, at::TensorOptions options={}) {
    return at::_ops::hann_window_periodic::call(window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hann_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::hann_window_periodic::call(window_length, periodic, dtype, layout, device, pin_memory);
}

// aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hamming_window(int64_t window_length, at::TensorOptions options={}) {
    return at::_ops::hamming_window::call(window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hamming_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::hamming_window::call(window_length, dtype, layout, device, pin_memory);
}

// aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hamming_window(int64_t window_length, bool periodic, at::TensorOptions options={}) {
    return at::_ops::hamming_window_periodic::call(window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hamming_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::hamming_window_periodic::call(window_length, periodic, dtype, layout, device, pin_memory);
}

// aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, at::TensorOptions options={}) {
    return at::_ops::hamming_window_periodic_alpha::call(window_length, periodic, alpha, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::hamming_window_periodic_alpha::call(window_length, periodic, alpha, dtype, layout, device, pin_memory);
}

// aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, at::TensorOptions options={}) {
    return at::_ops::hamming_window_periodic_alpha_beta::call(window_length, periodic, alpha, beta, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor hamming_window(int64_t window_length, bool periodic, double alpha, double beta, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::hamming_window_periodic_alpha_beta::call(window_length, periodic, alpha, beta, dtype, layout, device, pin_memory);
}

// aten::kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor kaiser_window(int64_t window_length, at::TensorOptions options={}) {
    return at::_ops::kaiser_window::call(window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor kaiser_window(int64_t window_length, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::kaiser_window::call(window_length, dtype, layout, device, pin_memory);
}

// aten::kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor kaiser_window(int64_t window_length, bool periodic, at::TensorOptions options={}) {
    return at::_ops::kaiser_window_periodic::call(window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor kaiser_window(int64_t window_length, bool periodic, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::kaiser_window_periodic::call(window_length, periodic, dtype, layout, device, pin_memory);
}

// aten::kaiser_window.beta(int window_length, bool periodic, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor kaiser_window(int64_t window_length, bool periodic, double beta, at::TensorOptions options={}) {
    return at::_ops::kaiser_window_beta::call(window_length, periodic, beta, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::kaiser_window.beta(int window_length, bool periodic, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor kaiser_window(int64_t window_length, bool periodic, double beta, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::kaiser_window_beta::call(window_length, periodic, beta, dtype, layout, device, pin_memory);
}

// aten::hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor
inline at::Tensor hinge_embedding_loss(const at::Tensor & self, const at::Tensor & target, double margin=1.0, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::hinge_embedding_loss::call(self, target, margin, reduction);
}

// aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor
inline at::Tensor group_norm(const at::Tensor & input, int64_t num_groups, const ::std::optional<at::Tensor> & weight={}, const ::std::optional<at::Tensor> & bias={}, double eps=1e-05, bool cudnn_enabled=true) {
    return at::_ops::group_norm::call(input, num_groups, weight, bias, eps, cudnn_enabled);
}

// aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps) {
    return at::_ops::native_group_norm::call(input, weight, bias, N, C, HxW, group, eps);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps) {
    return at::_ops::native_group_norm::call(input, weight, bias, N, C, HxW, group, eps);
  }
}

// aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_symint(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) {
    return at::_ops::native_group_norm::call(input, weight, bias, N, C, HxW, group, eps);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) {
    return at::_ops::native_group_norm::call(input, weight, bias, N, C, HxW, group, eps);
  }
}

// aten::native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array<bool,3> output_mask) {
    return at::_ops::native_group_norm_backward::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array<bool,3> output_mask) {
    return at::_ops::native_group_norm_backward::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask);
  }
}

// aten::native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward_symint(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask) {
    return at::_ops::native_group_norm_backward::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_group_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask) {
    return at::_ops::native_group_norm_backward::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask);
  }
}

// aten::_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor
inline at::Tensor _fft_r2c(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) {
    return at::_ops::_fft_r2c::call(self, dim, normalization, onesided);
}

// aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_r2c_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) {
    return at::_ops::_fft_r2c_out::call(self, dim, normalization, onesided, out);
}
// aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_r2c_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided, at::Tensor & out) {
    return at::_ops::_fft_r2c_out::call(self, dim, normalization, onesided, out);
}

// aten::_fft_c2r(Tensor self, int[] dim, int normalization, SymInt last_dim_size) -> Tensor
inline at::Tensor _fft_c2r(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) {
    return at::_ops::_fft_c2r::call(self, dim, normalization, last_dim_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _fft_c2r(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) {
    return at::_ops::_fft_c2r::call(self, dim, normalization, last_dim_size);
  }
}

// aten::_fft_c2r(Tensor self, int[] dim, int normalization, SymInt last_dim_size) -> Tensor
inline at::Tensor _fft_c2r_symint(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) {
    return at::_ops::_fft_c2r::call(self, dim, normalization, last_dim_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _fft_c2r(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) {
    return at::_ops::_fft_c2r::call(self, dim, normalization, last_dim_size);
  }
}

// aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_c2r_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) {
    return at::_ops::_fft_c2r_out::call(self, dim, normalization, last_dim_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _fft_c2r_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) {
    return at::_ops::_fft_c2r_out::call(self, dim, normalization, last_dim_size, out);
  }
}

// aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_c2r_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size, at::Tensor & out) {
    return at::_ops::_fft_c2r_out::call(self, dim, normalization, last_dim_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _fft_c2r_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size, at::Tensor & out) {
    return at::_ops::_fft_c2r_out::call(self, dim, normalization, last_dim_size, out);
  }
}

// aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_c2r_symint_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) {
    return at::_ops::_fft_c2r_out::call(self, dim, normalization, last_dim_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _fft_c2r_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) {
    return at::_ops::_fft_c2r_out::call(self, dim, normalization, last_dim_size, out);
  }
}

// aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_c2r_symint_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size, at::Tensor & out) {
    return at::_ops::_fft_c2r_out::call(self, dim, normalization, last_dim_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _fft_c2r_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size, at::Tensor & out) {
    return at::_ops::_fft_c2r_out::call(self, dim, normalization, last_dim_size, out);
  }
}

// aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor
inline at::Tensor _fft_c2c(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward) {
    return at::_ops::_fft_c2c::call(self, c10::fromIntArrayRefSlow(dim), normalization, forward);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _fft_c2c(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward) {
    return at::_ops::_fft_c2c::call(self, c10::fromIntArrayRefSlow(dim), normalization, forward);
  }
}

// aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor
inline at::Tensor _fft_c2c_symint(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) {
    return at::_ops::_fft_c2c::call(self, dim, normalization, forward);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _fft_c2c(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) {
    return at::_ops::_fft_c2c::call(self, dim, normalization, forward);
  }
}

// aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_c2c_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward) {
    return at::_ops::_fft_c2c_out::call(self, c10::fromIntArrayRefSlow(dim), normalization, forward, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _fft_c2c_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward) {
    return at::_ops::_fft_c2c_out::call(self, c10::fromIntArrayRefSlow(dim), normalization, forward, out);
  }
}

// aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_c2c_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) {
    return at::_ops::_fft_c2c_out::call(self, c10::fromIntArrayRefSlow(dim), normalization, forward, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _fft_c2c_outf(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) {
    return at::_ops::_fft_c2c_out::call(self, c10::fromIntArrayRefSlow(dim), normalization, forward, out);
  }
}

// aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_c2c_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) {
    return at::_ops::_fft_c2c_out::call(self, dim, normalization, forward, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _fft_c2c_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) {
    return at::_ops::_fft_c2c_out::call(self, dim, normalization, forward, out);
  }
}

// aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fft_c2c_symint_outf(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) {
    return at::_ops::_fft_c2c_out::call(self, dim, normalization, forward, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _fft_c2c_outf(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) {
    return at::_ops::_fft_c2c_out::call(self, dim, normalization, forward, out);
  }
}

// aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> ()
inline void _validate_compressed_sparse_indices(bool is_crow, const at::Tensor & compressed_idx, const at::Tensor & plain_idx, int64_t cdim, int64_t dim, int64_t nnz) {
    return at::_ops::_validate_compressed_sparse_indices::call(is_crow, compressed_idx, plain_idx, cdim, dim, nnz);
}

// aten::_cufft_get_plan_cache_size(DeviceIndex device_index) -> int
inline int64_t _cufft_get_plan_cache_size(at::DeviceIndex device_index) {
    return at::_ops::_cufft_get_plan_cache_size::call(device_index);
}

// aten::_cufft_get_plan_cache_max_size(DeviceIndex device_index) -> int
inline int64_t _cufft_get_plan_cache_max_size(at::DeviceIndex device_index) {
    return at::_ops::_cufft_get_plan_cache_max_size::call(device_index);
}

// aten::_cufft_set_plan_cache_max_size(DeviceIndex device_index, int max_size) -> ()
inline void _cufft_set_plan_cache_max_size(at::DeviceIndex device_index, int64_t max_size) {
    return at::_ops::_cufft_set_plan_cache_max_size::call(device_index, max_size);
}

// aten::_cufft_clear_plan_cache(DeviceIndex device_index) -> ()
inline void _cufft_clear_plan_cache(at::DeviceIndex device_index) {
    return at::_ops::_cufft_clear_plan_cache::call(device_index);
}

// aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor
inline at::Tensor index(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices) {
    return at::_ops::index_Tensor::call(self, indices);
}

// aten::index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_out(at::Tensor & out, const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices) {
    return at::_ops::index_Tensor_out::call(self, indices, out);
}
// aten::index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_outf(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, at::Tensor & out) {
    return at::_ops::index_Tensor_out::call(self, indices, out);
}

// aten::_unsafe_index.Tensor(Tensor self, Tensor?[] indices) -> Tensor
inline at::Tensor _unsafe_index(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices) {
    return at::_ops::_unsafe_index_Tensor::call(self, indices);
}

// aten::_unsafe_masked_index(Tensor self, Tensor mask, Tensor?[] indices, Scalar fill) -> Tensor
inline at::Tensor _unsafe_masked_index(const at::Tensor & self, const at::Tensor & mask, const c10::List<::std::optional<at::Tensor>> & indices, const at::Scalar & fill) {
    return at::_ops::_unsafe_masked_index::call(self, mask, indices, fill);
}

// aten::_unsafe_masked_index_put_accumulate(Tensor self, Tensor mask, Tensor?[] indices, Tensor values) -> Tensor
inline at::Tensor _unsafe_masked_index_put_accumulate(const at::Tensor & self, const at::Tensor & mask, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values) {
    return at::_ops::_unsafe_masked_index_put_accumulate::call(self, mask, indices, values);
}

// aten::index_copy.out(Tensor self, int dim, Tensor index, Tensor source, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) {
    return at::_ops::index_copy_out::call(self, dim, index, source, out);
}
// aten::index_copy.out(Tensor self, int dim, Tensor index, Tensor source, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_copy_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, at::Tensor & out) {
    return at::_ops::index_copy_out::call(self, dim, index, source, out);
}

// aten::index_copy(Tensor self, int dim, Tensor index, Tensor source) -> Tensor
inline at::Tensor index_copy(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) {
    return at::_ops::index_copy::call(self, dim, index, source);
}

// aten::index_copy.dimname(Tensor self, Dimname dim, Tensor index, Tensor source) -> Tensor
inline at::Tensor index_copy(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source) {
    return at::_ops::index_copy_dimname::call(self, dim, index, source);
}

// aten::index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!)
inline at::Tensor & index_put_(at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false) {
    return at::_ops::index_put_::call(self, indices, values, accumulate);
}

// aten::index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor
inline at::Tensor index_put(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false) {
    return at::_ops::index_put::call(self, indices, values, accumulate);
}

// aten::_unsafe_index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor
inline at::Tensor _unsafe_index_put(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false) {
    return at::_ops::_unsafe_index_put::call(self, indices, values, accumulate);
}

// aten::_index_put_impl_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor(a!)
inline at::Tensor & _index_put_impl_(at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false, bool unsafe=false) {
    return at::_ops::_index_put_impl_::call(self, indices, values, accumulate, unsafe);
}

// aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensor
inline at::Tensor instance_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) {
    return at::_ops::instance_norm::call(input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled);
}

// aten::isclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> Tensor
inline at::Tensor isclose(const at::Tensor & self, const at::Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false) {
    return at::_ops::isclose::call(self, other, rtol, atol, equal_nan);
}

// aten::isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isin_out(at::Tensor & out, const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) {
    return at::_ops::isin_Tensor_Tensor_out::call(elements, test_elements, assume_unique, invert, out);
}
// aten::isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isin_outf(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) {
    return at::_ops::isin_Tensor_Tensor_out::call(elements, test_elements, assume_unique, invert, out);
}

// aten::isin.Tensor_Tensor(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor
inline at::Tensor isin(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) {
    return at::_ops::isin_Tensor_Tensor::call(elements, test_elements, assume_unique, invert);
}

// aten::isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isin_out(at::Tensor & out, const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique=false, bool invert=false) {
    return at::_ops::isin_Tensor_Scalar_out::call(elements, test_element, assume_unique, invert, out);
}
// aten::isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isin_outf(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert, at::Tensor & out) {
    return at::_ops::isin_Tensor_Scalar_out::call(elements, test_element, assume_unique, invert, out);
}

// aten::isin.Tensor_Scalar(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False) -> Tensor
inline at::Tensor isin(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique=false, bool invert=false) {
    return at::_ops::isin_Tensor_Scalar::call(elements, test_element, assume_unique, invert);
}

// aten::isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isin_out(at::Tensor & out, const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) {
    return at::_ops::isin_Scalar_Tensor_out::call(element, test_elements, assume_unique, invert, out);
}
// aten::isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isin_outf(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) {
    return at::_ops::isin_Scalar_Tensor_out::call(element, test_elements, assume_unique, invert, out);
}

// aten::isin.Scalar_Tensor(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor
inline at::Tensor isin(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) {
    return at::_ops::isin_Scalar_Tensor::call(element, test_elements, assume_unique, invert);
}

// aten::isnan(Tensor self) -> Tensor
inline at::Tensor isnan(const at::Tensor & self) {
    return at::_ops::isnan::call(self);
}

// aten::is_distributed(Tensor self) -> bool
inline bool is_distributed(const at::Tensor & self) {
    return at::_ops::is_distributed::call(self);
}

// aten::is_floating_point(Tensor self) -> bool
inline bool __dispatch_is_floating_point(const at::Tensor & self) {
    return at::_ops::is_floating_point::call(self);
}

// aten::is_complex(Tensor self) -> bool
inline bool __dispatch_is_complex(const at::Tensor & self) {
    return at::_ops::is_complex::call(self);
}

// aten::is_conj(Tensor self) -> bool
inline bool __dispatch_is_conj(const at::Tensor & self) {
    return at::_ops::is_conj::call(self);
}

// aten::_is_zerotensor(Tensor self) -> bool
inline bool __dispatch__is_zerotensor(const at::Tensor & self) {
    return at::_ops::_is_zerotensor::call(self);
}

// aten::is_neg(Tensor self) -> bool
inline bool __dispatch_is_neg(const at::Tensor & self) {
    return at::_ops::is_neg::call(self);
}

// aten::isreal(Tensor self) -> Tensor
inline at::Tensor isreal(const at::Tensor & self) {
    return at::_ops::isreal::call(self);
}

// aten::is_nonzero(Tensor self) -> bool
inline bool is_nonzero(const at::Tensor & self) {
    return at::_ops::is_nonzero::call(self);
}

// aten::is_same_size(Tensor self, Tensor other) -> bool
inline bool is_same_size(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::is_same_size::call(self, other);
}

// aten::is_signed(Tensor self) -> bool
inline bool __dispatch_is_signed(const at::Tensor & self) {
    return at::_ops::is_signed::call(self);
}

// aten::is_inference(Tensor self) -> bool
inline bool __dispatch_is_inference(const at::Tensor & self) {
    return at::_ops::is_inference::call(self);
}

// aten::kl_div(Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor
inline at::Tensor kl_div(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, bool log_target=false) {
    return at::_ops::kl_div::call(self, target, reduction, log_target);
}

// aten::kron(Tensor self, Tensor other) -> Tensor
inline at::Tensor kron(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::kron::call(self, other);
}

// aten::kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & kron_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::kron_out::call(self, other, out);
}
// aten::kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & kron_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::kron_out::call(self, other, out);
}

// aten::kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> kthvalue(const at::Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false) {
    return at::_ops::kthvalue::call(self, k, dim, keepdim);
}

// aten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false) {
    return at::_ops::kthvalue_values::call(self, k, dim, keepdim, values, indices);
}
// aten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_outf(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::kthvalue_values::call(self, k, dim, keepdim, values, indices);
}

// aten::kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> kthvalue(const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim=false) {
    return at::_ops::kthvalue_dimname::call(self, k, dim, keepdim);
}

// aten::kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim=false) {
    return at::_ops::kthvalue_dimname_out::call(self, k, dim, keepdim, values, indices);
}
// aten::kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> kthvalue_outf(const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::kthvalue_dimname_out::call(self, k, dim, keepdim, values, indices);
}

// aten::layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor
inline at::Tensor layer_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight={}, const ::std::optional<at::Tensor> & bias={}, double eps=1e-05, bool cudnn_enable=true) {
    return at::_ops::layer_norm::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, cudnn_enable);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor layer_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight={}, const ::std::optional<at::Tensor> & bias={}, double eps=1e-05, bool cudnn_enable=true) {
    return at::_ops::layer_norm::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, cudnn_enable);
  }
}

// aten::layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor
inline at::Tensor layer_norm_symint(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight={}, const ::std::optional<at::Tensor> & bias={}, double eps=1e-05, bool cudnn_enable=true) {
    return at::_ops::layer_norm::call(input, normalized_shape, weight, bias, eps, cudnn_enable);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor layer_norm(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight={}, const ::std::optional<at::Tensor> & bias={}, double eps=1e-05, bool cudnn_enable=true) {
    return at::_ops::layer_norm::call(input, normalized_shape, weight, bias, eps, cudnn_enable);
  }
}

// aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps) {
    return at::_ops::native_layer_norm::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps) {
    return at::_ops::native_layer_norm::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps);
  }
}

// aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_symint(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps) {
    return at::_ops::native_layer_norm::call(input, normalized_shape, weight, bias, eps);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps) {
    return at::_ops::native_layer_norm::call(input, normalized_shape, weight, bias, eps);
  }
}

// aten::native_layer_norm_backward(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) {
    return at::_ops::native_layer_norm_backward::call(grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) {
    return at::_ops::native_layer_norm_backward::call(grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask);
  }
}

// aten::native_layer_norm_backward(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_backward_symint(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) {
    return at::_ops::native_layer_norm_backward::call(grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_layer_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) {
    return at::_ops::native_layer_norm_backward::call(grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask);
  }
}

// aten::rms_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, float? eps=None) -> Tensor
inline at::Tensor rms_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight={}, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::rms_norm::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, eps);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rms_norm(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight={}, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::rms_norm::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, eps);
  }
}

// aten::rms_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, float? eps=None) -> Tensor
inline at::Tensor rms_norm_symint(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight={}, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::rms_norm::call(input, normalized_shape, weight, eps);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rms_norm(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight={}, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::rms_norm::call(input, normalized_shape, weight, eps);
  }
}

// aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor
inline at::Tensor nan_to_num(const at::Tensor & self, ::std::optional<double> nan=::std::nullopt, ::std::optional<double> posinf=::std::nullopt, ::std::optional<double> neginf=::std::nullopt) {
    return at::_ops::nan_to_num::call(self, nan, posinf, neginf);
}

// aten::nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!)
inline at::Tensor & nan_to_num_(at::Tensor & self, ::std::optional<double> nan=::std::nullopt, ::std::optional<double> posinf=::std::nullopt, ::std::optional<double> neginf=::std::nullopt) {
    return at::_ops::nan_to_num_::call(self, nan, posinf, neginf);
}

// aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nan_to_num_out(at::Tensor & out, const at::Tensor & self, ::std::optional<double> nan=::std::nullopt, ::std::optional<double> posinf=::std::nullopt, ::std::optional<double> neginf=::std::nullopt) {
    return at::_ops::nan_to_num_out::call(self, nan, posinf, neginf, out);
}
// aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nan_to_num_outf(const at::Tensor & self, ::std::optional<double> nan, ::std::optional<double> posinf, ::std::optional<double> neginf, at::Tensor & out) {
    return at::_ops::nan_to_num_out::call(self, nan, posinf, neginf, out);
}

// aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
inline at::Tensor linear(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}) {
    return at::_ops::linear::call(input, weight, bias);
}

// aten::linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linear_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask) {
    return at::_ops::linear_backward::call(self, grad_output, weight, output_mask);
}

// aten::linear.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linear_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}) {
    return at::_ops::linear_out::call(input, weight, bias, out);
}
// aten::linear.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linear_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & out) {
    return at::_ops::linear_out::call(input, weight, bias, out);
}

// aten::mkldnn_linear(Tensor self, Tensor weight, Tensor? bias=None) -> Tensor
inline at::Tensor mkldnn_linear(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}) {
    return at::_ops::mkldnn_linear::call(self, weight, bias);
}

// aten::mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor
inline at::Tensor mkldnn_linear_backward_input(at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight) {
    return at::_ops::mkldnn_linear_backward_input::call(input_size, grad_output, weight);
}

// aten::mkldnn_linear_backward_weights(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> mkldnn_linear_backward_weights(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined) {
    return at::_ops::mkldnn_linear_backward_weights::call(grad_output, input, weight, bias_defined);
}

// aten::mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> mkldnn_linear_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask) {
    return at::_ops::mkldnn_linear_backward::call(self, grad_output, weight, output_mask);
}

// aten::_cslt_compress(Tensor input) -> Tensor
inline at::Tensor _cslt_compress(const at::Tensor & input) {
    return at::_ops::_cslt_compress::call(input);
}

// aten::_cslt_sparse_mm(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False, int alg_id=0) -> Tensor
inline at::Tensor _cslt_sparse_mm(const at::Tensor & compressed_A, const at::Tensor & dense_B, const ::std::optional<at::Tensor> & bias={}, const ::std::optional<at::Tensor> & alpha={}, ::std::optional<at::ScalarType> out_dtype=::std::nullopt, bool transpose_result=false, int64_t alg_id=0) {
    return at::_ops::_cslt_sparse_mm::call(compressed_A, dense_B, bias, alpha, out_dtype, transpose_result, alg_id);
}

// aten::_cslt_sparse_mm_search(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False) -> int
inline int64_t _cslt_sparse_mm_search(const at::Tensor & compressed_A, const at::Tensor & dense_B, const ::std::optional<at::Tensor> & bias={}, const ::std::optional<at::Tensor> & alpha={}, ::std::optional<at::ScalarType> out_dtype=::std::nullopt, bool transpose_result=false) {
    return at::_ops::_cslt_sparse_mm_search::call(compressed_A, dense_B, bias, alpha, out_dtype, transpose_result);
}

// aten::_sparse_semi_structured_tile(Tensor input, str algorithm="", bool use_cutlass=True) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _sparse_semi_structured_tile(const at::Tensor & input, c10::string_view algorithm="", bool use_cutlass=true) {
    return at::_ops::_sparse_semi_structured_tile::call(input, algorithm, use_cutlass);
}

// aten::_sparse_semi_structured_apply(Tensor input, Tensor thread_masks) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _sparse_semi_structured_apply(const at::Tensor & input, const at::Tensor & thread_masks) {
    return at::_ops::_sparse_semi_structured_apply::call(input, thread_masks);
}

// aten::_sparse_semi_structured_apply_dense(Tensor input, Tensor thread_masks) -> Tensor
inline at::Tensor _sparse_semi_structured_apply_dense(const at::Tensor & input, const at::Tensor & thread_masks) {
    return at::_ops::_sparse_semi_structured_apply_dense::call(input, thread_masks);
}

// aten::_sparse_semi_structured_linear(Tensor input, Tensor weight, Tensor meta, *, Tensor? bias=None, str? activation=None, ScalarType? out_dtype=None) -> Tensor
inline at::Tensor _sparse_semi_structured_linear(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & meta, const ::std::optional<at::Tensor> & bias={}, ::std::optional<c10::string_view> activation=::std::nullopt, ::std::optional<at::ScalarType> out_dtype=::std::nullopt) {
    return at::_ops::_sparse_semi_structured_linear::call(input, weight, meta, bias, activation, out_dtype);
}

// aten::_sparse_semi_structured_mm(Tensor mat1, Tensor mat1_meta, Tensor mat2, *, ScalarType? out_dtype=None) -> Tensor
inline at::Tensor _sparse_semi_structured_mm(const at::Tensor & mat1, const at::Tensor & mat1_meta, const at::Tensor & mat2, ::std::optional<at::ScalarType> out_dtype=::std::nullopt) {
    return at::_ops::_sparse_semi_structured_mm::call(mat1, mat1_meta, mat2, out_dtype);
}

// aten::_sparse_semi_structured_addmm(Tensor input, Tensor mat1, Tensor mat1_meta, Tensor mat2, *, Scalar alpha=1, Scalar beta=1, ScalarType? out_dtype=None) -> Tensor
inline at::Tensor _sparse_semi_structured_addmm(const at::Tensor & input, const at::Tensor & mat1, const at::Tensor & mat1_meta, const at::Tensor & mat2, const at::Scalar & alpha=1, const at::Scalar & beta=1, ::std::optional<at::ScalarType> out_dtype=::std::nullopt) {
    return at::_ops::_sparse_semi_structured_addmm::call(input, mat1, mat1_meta, mat2, alpha, beta, out_dtype);
}

// aten::_mixed_dtypes_linear(Tensor input, Tensor weight, Tensor scale, *, Tensor? bias=None, str? activation=None) -> Tensor
inline at::Tensor _mixed_dtypes_linear(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & scale, const ::std::optional<at::Tensor> & bias={}, ::std::optional<c10::string_view> activation=::std::nullopt) {
    return at::_ops::_mixed_dtypes_linear::call(input, weight, scale, bias, activation);
}

// aten::fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor
inline at::Tensor fbgemm_linear_int8_weight_fp32_activation(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) {
    return at::_ops::fbgemm_linear_int8_weight_fp32_activation::call(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias);
}

// aten::fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor
inline at::Tensor fbgemm_linear_int8_weight(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) {
    return at::_ops::fbgemm_linear_int8_weight::call(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias);
}

// aten::fbgemm_linear_quantize_weight(Tensor input) -> (Tensor, Tensor, float, int)
inline ::std::tuple<at::Tensor,at::Tensor,double,int64_t> fbgemm_linear_quantize_weight(const at::Tensor & input) {
    return at::_ops::fbgemm_linear_quantize_weight::call(input);
}

// aten::fbgemm_pack_gemm_matrix_fp16(Tensor input) -> Tensor
inline at::Tensor fbgemm_pack_gemm_matrix_fp16(const at::Tensor & input) {
    return at::_ops::fbgemm_pack_gemm_matrix_fp16::call(input);
}

// aten::_wrapped_linear_prepack(Tensor weight, Tensor weight_scale, Tensor weight_zero_point, Tensor bias) -> Tensor
inline at::Tensor _wrapped_linear_prepack(const at::Tensor & weight, const at::Tensor & weight_scale, const at::Tensor & weight_zero_point, const at::Tensor & bias) {
    return at::_ops::_wrapped_linear_prepack::call(weight, weight_scale, weight_zero_point, bias);
}

// aten::_wrapped_quantized_linear_prepacked(Tensor input, Tensor input_scale, Tensor input_zero_point, Tensor packed_weight, Tensor output_scale, Tensor output_zero_point, int out_channel) -> Tensor
inline at::Tensor _wrapped_quantized_linear_prepacked(const at::Tensor & input, const at::Tensor & input_scale, const at::Tensor & input_zero_point, const at::Tensor & packed_weight, const at::Tensor & output_scale, const at::Tensor & output_zero_point, int64_t out_channel) {
    return at::_ops::_wrapped_quantized_linear_prepacked::call(input, input_scale, input_zero_point, packed_weight, output_scale, output_zero_point, out_channel);
}

// aten::fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor
inline at::Tensor fbgemm_linear_fp16_weight_fp32_activation(const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) {
    return at::_ops::fbgemm_linear_fp16_weight_fp32_activation::call(input, packed_weight, bias);
}

// aten::fbgemm_linear_fp16_weight(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor
inline at::Tensor fbgemm_linear_fp16_weight(const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) {
    return at::_ops::fbgemm_linear_fp16_weight::call(input, packed_weight, bias);
}

// aten::fbgemm_pack_quantized_matrix(Tensor input) -> Tensor
inline at::Tensor fbgemm_pack_quantized_matrix(const at::Tensor & input) {
    return at::_ops::fbgemm_pack_quantized_matrix::call(input);
}

// aten::fbgemm_pack_quantized_matrix.KN(Tensor input, int K, int N) -> Tensor
inline at::Tensor fbgemm_pack_quantized_matrix(const at::Tensor & input, int64_t K, int64_t N) {
    return at::_ops::fbgemm_pack_quantized_matrix_KN::call(input, K, N);
}

// aten::ldexp.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor ldexp(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::ldexp_Tensor::call(self, other);
}

// aten::ldexp_(Tensor(a!) self, Tensor other) -> Tensor(a!)
inline at::Tensor & ldexp_(at::Tensor & self, const at::Tensor & other) {
    return at::_ops::ldexp_::call(self, other);
}

// aten::ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ldexp_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::ldexp_out::call(self, other, out);
}
// aten::ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ldexp_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::ldexp_out::call(self, other, out);
}

// aten::linspace(Scalar start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor linspace(const at::Scalar & start, const at::Scalar & end, int64_t steps, at::TensorOptions options={}) {
    return at::_ops::linspace::call(start, end, steps, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::linspace(Scalar start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor linspace(const at::Scalar & start, const at::Scalar & end, int64_t steps, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::linspace::call(start, end, steps, dtype, layout, device, pin_memory);
}

// aten::linspace.Tensor_Tensor(Tensor start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor linspace(const at::Tensor & start, const at::Tensor & end, int64_t steps, at::TensorOptions options={}) {
    return at::_ops::linspace_Tensor_Tensor::call(start, end, steps, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::linspace.Tensor_Tensor(Tensor start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor linspace(const at::Tensor & start, const at::Tensor & end, int64_t steps, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::linspace_Tensor_Tensor::call(start, end, steps, dtype, layout, device, pin_memory);
}

// aten::linspace.Tensor_Scalar(Tensor start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor linspace(const at::Tensor & start, const at::Scalar & end, int64_t steps, at::TensorOptions options={}) {
    return at::_ops::linspace_Tensor_Scalar::call(start, end, steps, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::linspace.Tensor_Scalar(Tensor start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor linspace(const at::Tensor & start, const at::Scalar & end, int64_t steps, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::linspace_Tensor_Scalar::call(start, end, steps, dtype, layout, device, pin_memory);
}

// aten::linspace.Scalar_Tensor(Scalar start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor linspace(const at::Scalar & start, const at::Tensor & end, int64_t steps, at::TensorOptions options={}) {
    return at::_ops::linspace_Scalar_Tensor::call(start, end, steps, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::linspace.Scalar_Tensor(Scalar start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor linspace(const at::Scalar & start, const at::Tensor & end, int64_t steps, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::linspace_Scalar_Tensor::call(start, end, steps, dtype, layout, device, pin_memory);
}

// aten::linspace.out(Scalar start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linspace_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end, int64_t steps) {
    return at::_ops::linspace_out::call(start, end, steps, out);
}
// aten::linspace.out(Scalar start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linspace_outf(const at::Scalar & start, const at::Scalar & end, int64_t steps, at::Tensor & out) {
    return at::_ops::linspace_out::call(start, end, steps, out);
}

// aten::linspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linspace_out(at::Tensor & out, const at::Tensor & start, const at::Tensor & end, int64_t steps) {
    return at::_ops::linspace_Tensor_Tensor_out::call(start, end, steps, out);
}
// aten::linspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linspace_outf(const at::Tensor & start, const at::Tensor & end, int64_t steps, at::Tensor & out) {
    return at::_ops::linspace_Tensor_Tensor_out::call(start, end, steps, out);
}

// aten::linspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linspace_out(at::Tensor & out, const at::Tensor & start, const at::Scalar & end, int64_t steps) {
    return at::_ops::linspace_Tensor_Scalar_out::call(start, end, steps, out);
}
// aten::linspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linspace_outf(const at::Tensor & start, const at::Scalar & end, int64_t steps, at::Tensor & out) {
    return at::_ops::linspace_Tensor_Scalar_out::call(start, end, steps, out);
}

// aten::linspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linspace_out(at::Tensor & out, const at::Scalar & start, const at::Tensor & end, int64_t steps) {
    return at::_ops::linspace_Scalar_Tensor_out::call(start, end, steps, out);
}
// aten::linspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linspace_outf(const at::Scalar & start, const at::Tensor & end, int64_t steps, at::Tensor & out) {
    return at::_ops::linspace_Scalar_Tensor_out::call(start, end, steps, out);
}

// aten::log(Tensor self) -> Tensor
inline at::Tensor log(const at::Tensor & self) {
    return at::_ops::log::call(self);
}

// aten::log_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & log_(at::Tensor & self) {
    return at::_ops::log_::call(self);
}

// aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::log_out::call(self, out);
}
// aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::log_out::call(self, out);
}

// aten::log10(Tensor self) -> Tensor
inline at::Tensor log10(const at::Tensor & self) {
    return at::_ops::log10::call(self);
}

// aten::log10_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & log10_(at::Tensor & self) {
    return at::_ops::log10_::call(self);
}

// aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log10_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::log10_out::call(self, out);
}
// aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log10_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::log10_out::call(self, out);
}

// aten::log1p(Tensor self) -> Tensor
inline at::Tensor log1p(const at::Tensor & self) {
    return at::_ops::log1p::call(self);
}

// aten::log1p_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & log1p_(at::Tensor & self) {
    return at::_ops::log1p_::call(self);
}

// aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log1p_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::log1p_out::call(self, out);
}
// aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log1p_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::log1p_out::call(self, out);
}

// aten::log2(Tensor self) -> Tensor
inline at::Tensor log2(const at::Tensor & self) {
    return at::_ops::log2::call(self);
}

// aten::log2_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & log2_(at::Tensor & self) {
    return at::_ops::log2_::call(self);
}

// aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log2_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::log2_out::call(self, out);
}
// aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log2_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::log2_out::call(self, out);
}

// aten::logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logaddexp_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logaddexp_out::call(self, other, out);
}
// aten::logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logaddexp_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::logaddexp_out::call(self, other, out);
}

// aten::logaddexp(Tensor self, Tensor other) -> Tensor
inline at::Tensor logaddexp(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logaddexp::call(self, other);
}

// aten::logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logaddexp2_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logaddexp2_out::call(self, other, out);
}
// aten::logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logaddexp2_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::logaddexp2_out::call(self, other, out);
}

// aten::logaddexp2(Tensor self, Tensor other) -> Tensor
inline at::Tensor logaddexp2(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::logaddexp2::call(self, other);
}

// aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::xlogy_Tensor::call(self, other);
}

// aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor
inline at::Tensor xlogy(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::xlogy_Scalar_Self::call(self, other);
}

// aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor
inline at::Tensor xlogy(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::xlogy_Scalar_Other::call(self, other);
}

// aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
inline at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other) {
    return at::_ops::xlogy__Tensor::call(self, other);
}

// aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)
inline at::Tensor & xlogy_(at::Tensor & self, const at::Scalar & other) {
    return at::_ops::xlogy__Scalar_Other::call(self, other);
}

// aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::xlogy_OutTensor::call(self, other, out);
}
// aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::xlogy_OutTensor::call(self, other, out);
}

// aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::xlogy_OutScalar_Self::call(self, other, out);
}
// aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::xlogy_OutScalar_Self::call(self, other, out);
}

// aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::xlogy_OutScalar_Other::call(self, other, out);
}
// aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::xlogy_OutScalar_Other::call(self, other, out);
}

// aten::logspace(Scalar start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor logspace(const at::Scalar & start, const at::Scalar & end, int64_t steps, double base=10.0, at::TensorOptions options={}) {
    return at::_ops::logspace::call(start, end, steps, base, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::logspace(Scalar start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor logspace(const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::logspace::call(start, end, steps, base, dtype, layout, device, pin_memory);
}

// aten::logspace.Tensor_Tensor(Tensor start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor logspace(const at::Tensor & start, const at::Tensor & end, int64_t steps, double base=10.0, at::TensorOptions options={}) {
    return at::_ops::logspace_Tensor_Tensor::call(start, end, steps, base, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::logspace.Tensor_Tensor(Tensor start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor logspace(const at::Tensor & start, const at::Tensor & end, int64_t steps, double base, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::logspace_Tensor_Tensor::call(start, end, steps, base, dtype, layout, device, pin_memory);
}

// aten::logspace.Tensor_Scalar(Tensor start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor logspace(const at::Tensor & start, const at::Scalar & end, int64_t steps, double base=10.0, at::TensorOptions options={}) {
    return at::_ops::logspace_Tensor_Scalar::call(start, end, steps, base, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::logspace.Tensor_Scalar(Tensor start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor logspace(const at::Tensor & start, const at::Scalar & end, int64_t steps, double base, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::logspace_Tensor_Scalar::call(start, end, steps, base, dtype, layout, device, pin_memory);
}

// aten::logspace.Scalar_Tensor(Scalar start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor logspace(const at::Scalar & start, const at::Tensor & end, int64_t steps, double base=10.0, at::TensorOptions options={}) {
    return at::_ops::logspace_Scalar_Tensor::call(start, end, steps, base, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::logspace.Scalar_Tensor(Scalar start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor logspace(const at::Scalar & start, const at::Tensor & end, int64_t steps, double base, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::logspace_Scalar_Tensor::call(start, end, steps, base, dtype, layout, device, pin_memory);
}

// aten::logspace.out(Scalar start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logspace_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end, int64_t steps, double base=10.0) {
    return at::_ops::logspace_out::call(start, end, steps, base, out);
}
// aten::logspace.out(Scalar start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logspace_outf(const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, at::Tensor & out) {
    return at::_ops::logspace_out::call(start, end, steps, base, out);
}

// aten::logspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logspace_out(at::Tensor & out, const at::Tensor & start, const at::Tensor & end, int64_t steps, double base=10.0) {
    return at::_ops::logspace_Tensor_Tensor_out::call(start, end, steps, base, out);
}
// aten::logspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logspace_outf(const at::Tensor & start, const at::Tensor & end, int64_t steps, double base, at::Tensor & out) {
    return at::_ops::logspace_Tensor_Tensor_out::call(start, end, steps, base, out);
}

// aten::logspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logspace_out(at::Tensor & out, const at::Tensor & start, const at::Scalar & end, int64_t steps, double base=10.0) {
    return at::_ops::logspace_Tensor_Scalar_out::call(start, end, steps, base, out);
}
// aten::logspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logspace_outf(const at::Tensor & start, const at::Scalar & end, int64_t steps, double base, at::Tensor & out) {
    return at::_ops::logspace_Tensor_Scalar_out::call(start, end, steps, base, out);
}

// aten::logspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logspace_out(at::Tensor & out, const at::Scalar & start, const at::Tensor & end, int64_t steps, double base=10.0) {
    return at::_ops::logspace_Scalar_Tensor_out::call(start, end, steps, base, out);
}
// aten::logspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logspace_outf(const at::Scalar & start, const at::Tensor & end, int64_t steps, double base, at::Tensor & out) {
    return at::_ops::logspace_Scalar_Tensor_out::call(start, end, steps, base, out);
}

// aten::log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
inline at::Tensor log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::log_softmax_int::call(self, dim, dtype);
}

// aten::log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log_softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::log_softmax_int_out::call(self, dim, dtype, out);
}
// aten::log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log_softmax_outf(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::log_softmax_int_out::call(self, dim, dtype, out);
}

// aten::log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor log_softmax(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::log_softmax_Dimname::call(self, dim, dtype);
}

// aten::_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
inline at::Tensor _log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) {
    return at::_ops::_log_softmax::call(self, dim, half_to_float);
}

// aten::_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _log_softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) {
    return at::_ops::_log_softmax_out::call(self, dim, half_to_float, out);
}
// aten::_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _log_softmax_outf(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) {
    return at::_ops::_log_softmax_out::call(self, dim, half_to_float, out);
}

// aten::_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor
inline at::Tensor _log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) {
    return at::_ops::_log_softmax_backward_data::call(grad_output, output, dim, input_dtype);
}

// aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _log_softmax_backward_data_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) {
    return at::_ops::_log_softmax_backward_data_out::call(grad_output, output, dim, input_dtype, out);
}
// aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _log_softmax_backward_data_outf(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & out) {
    return at::_ops::_log_softmax_backward_data_out::call(grad_output, output, dim, input_dtype, out);
}

// aten::_logcumsumexp(Tensor self, int dim) -> Tensor
inline at::Tensor _logcumsumexp(const at::Tensor & self, int64_t dim) {
    return at::_ops::_logcumsumexp::call(self, dim);
}

// aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _logcumsumexp_out(at::Tensor & out, const at::Tensor & self, int64_t dim) {
    return at::_ops::_logcumsumexp_out::call(self, dim, out);
}
// aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _logcumsumexp_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) {
    return at::_ops::_logcumsumexp_out::call(self, dim, out);
}

// aten::logcumsumexp(Tensor self, int dim) -> Tensor
inline at::Tensor logcumsumexp(const at::Tensor & self, int64_t dim) {
    return at::_ops::logcumsumexp::call(self, dim);
}

// aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logcumsumexp_out(at::Tensor & out, const at::Tensor & self, int64_t dim) {
    return at::_ops::logcumsumexp_out::call(self, dim, out);
}
// aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logcumsumexp_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) {
    return at::_ops::logcumsumexp_out::call(self, dim, out);
}

// aten::logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor
inline at::Tensor logcumsumexp(const at::Tensor & self, at::Dimname dim) {
    return at::_ops::logcumsumexp_dimname::call(self, dim);
}

// aten::logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logcumsumexp_out(at::Tensor & out, const at::Tensor & self, at::Dimname dim) {
    return at::_ops::logcumsumexp_dimname_out::call(self, dim, out);
}
// aten::logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logcumsumexp_outf(const at::Tensor & self, at::Dimname dim, at::Tensor & out) {
    return at::_ops::logcumsumexp_dimname_out::call(self, dim, out);
}

// aten::logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor
inline at::Tensor logsumexp(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::logsumexp::call(self, dim, keepdim);
}

// aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logsumexp_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::logsumexp_out::call(self, dim, keepdim, out);
}
// aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logsumexp_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::logsumexp_out::call(self, dim, keepdim, out);
}

// aten::logsumexp.names(Tensor self, Dimname[1] dim, bool keepdim=False) -> Tensor
inline at::Tensor logsumexp(const at::Tensor & self, at::DimnameList dim, bool keepdim=false) {
    return at::_ops::logsumexp_names::call(self, dim, keepdim);
}

// aten::logsumexp.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logsumexp_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool keepdim=false) {
    return at::_ops::logsumexp_names_out::call(self, dim, keepdim, out);
}
// aten::logsumexp.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logsumexp_outf(const at::Tensor & self, at::DimnameList dim, bool keepdim, at::Tensor & out) {
    return at::_ops::logsumexp_names_out::call(self, dim, keepdim, out);
}

// aten::margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor
inline at::Tensor margin_ranking_loss(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin=0.0, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::margin_ranking_loss::call(input1, input2, target, margin, reduction);
}

// aten::matmul(Tensor self, Tensor other) -> Tensor
inline at::Tensor matmul(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::matmul::call(self, other);
}

// aten::matmul_backward(Tensor grad, Tensor self, Tensor other, bool[2] mask) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> matmul_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array<bool,2> mask) {
    return at::_ops::matmul_backward::call(grad, self, other, mask);
}

// aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & matmul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::matmul_out::call(self, other, out);
}
// aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & matmul_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::matmul_out::call(self, other, out);
}

// aten::matrix_power(Tensor self, int n) -> Tensor
inline at::Tensor matrix_power(const at::Tensor & self, int64_t n) {
    return at::_ops::matrix_power::call(self, n);
}

// aten::matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & matrix_power_out(at::Tensor & out, const at::Tensor & self, int64_t n) {
    return at::_ops::matrix_power_out::call(self, n, out);
}
// aten::matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & matrix_power_outf(const at::Tensor & self, int64_t n, at::Tensor & out) {
    return at::_ops::matrix_power_out::call(self, n, out);
}

// aten::matrix_exp(Tensor self) -> Tensor
inline at::Tensor matrix_exp(const at::Tensor & self) {
    return at::_ops::matrix_exp::call(self);
}

// aten::matrix_exp_backward(Tensor self, Tensor grad) -> Tensor
inline at::Tensor matrix_exp_backward(const at::Tensor & self, const at::Tensor & grad) {
    return at::_ops::matrix_exp_backward::call(self, grad);
}

// aten::_aminmax(Tensor self) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _aminmax(const at::Tensor & self) {
    return at::_ops::_aminmax::call(self);
}

// aten::_aminmax.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _aminmax(const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::_aminmax_dim::call(self, dim, keepdim);
}

// aten::aminmax(Tensor self, *, int? dim=None, bool keepdim=False) -> (Tensor min, Tensor max)
inline ::std::tuple<at::Tensor,at::Tensor> aminmax(const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false) {
    return at::_ops::aminmax::call(self, dim, keepdim);
}

// aten::aminmax.out(Tensor self, *, int? dim=None, bool keepdim=False, Tensor(a!) min, Tensor(b!) max) -> (Tensor(a!) min, Tensor(b!) max)
inline ::std::tuple<at::Tensor &,at::Tensor &> aminmax_out(at::Tensor & min, at::Tensor & max, const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false) {
    return at::_ops::aminmax_out::call(self, dim, keepdim, min, max);
}
// aten::aminmax.out(Tensor self, *, int? dim=None, bool keepdim=False, Tensor(a!) min, Tensor(b!) max) -> (Tensor(a!) min, Tensor(b!) max)
inline ::std::tuple<at::Tensor &,at::Tensor &> aminmax_outf(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & min, at::Tensor & max) {
    return at::_ops::aminmax_out::call(self, dim, keepdim, min, max);
}

// aten::_compute_linear_combination(Tensor input, Tensor coefficients) -> Tensor
inline at::Tensor _compute_linear_combination(const at::Tensor & input, const at::Tensor & coefficients) {
    return at::_ops::_compute_linear_combination::call(input, coefficients);
}

// aten::_compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _compute_linear_combination_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & coefficients) {
    return at::_ops::_compute_linear_combination_out::call(input, coefficients, out);
}
// aten::_compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _compute_linear_combination_outf(const at::Tensor & input, const at::Tensor & coefficients, at::Tensor & out) {
    return at::_ops::_compute_linear_combination_out::call(input, coefficients, out);
}

// aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> max(const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::max_dim::call(self, dim, keepdim);
}

// aten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> max_out(at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::max_dim_max::call(self, dim, keepdim, max, max_values);
}
// aten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> max_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & max, at::Tensor & max_values) {
    return at::_ops::max_dim_max::call(self, dim, keepdim, max, max_values);
}

// aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> max(const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::max_names_dim::call(self, dim, keepdim);
}

// aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> max_out(at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::max_names_dim_max::call(self, dim, keepdim, max, max_values);
}
// aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> max_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & max, at::Tensor & max_values) {
    return at::_ops::max_names_dim_max::call(self, dim, keepdim, max, max_values);
}

// aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor
inline at::Tensor value_selecting_reduction_backward(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, at::IntArrayRef sizes, bool keepdim) {
    return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, c10::fromIntArrayRefSlow(sizes), keepdim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor value_selecting_reduction_backward(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, at::IntArrayRef sizes, bool keepdim) {
    return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, c10::fromIntArrayRefSlow(sizes), keepdim);
  }
}

// aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor
inline at::Tensor value_selecting_reduction_backward_symint(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim) {
    return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, sizes, keepdim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor value_selecting_reduction_backward(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim) {
    return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, sizes, keepdim);
  }
}

// aten::amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
inline at::Tensor amax(const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false) {
    return at::_ops::amax::call(self, dim, keepdim);
}

// aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & amax_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false) {
    return at::_ops::amax_out::call(self, dim, keepdim, out);
}
// aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & amax_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::amax_out::call(self, dim, keepdim, out);
}

// aten::max_pool1d_with_indices(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> max_pool1d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool1d_with_indices::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor max_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool1d::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool2d::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool2d_backward::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor mkldnn_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::mkldnn_max_pool2d::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::mkldnn_max_pool2d_backward(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor mkldnn_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::mkldnn_max_pool2d_backward::call(grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::mkldnn_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor mkldnn_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::mkldnn_max_pool3d::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::mkldnn_max_pool3d_backward(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor mkldnn_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::mkldnn_max_pool3d_backward::call(grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::quantized_max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor quantized_max_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::quantized_max_pool1d::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor quantized_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::quantized_max_pool2d::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::quantized_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor quantized_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::quantized_max_pool3d::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor
inline at::Tensor max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool3d::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor mean(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::mean::call(self, dtype);
}

// aten::mean.dtype_out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mean_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::mean_dtype_out::call(self, dtype, out);
}
// aten::mean.dtype_out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mean_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::mean_dtype_out::call(self, dtype, out);
}

// aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::mean_dim::call(self, dim, keepdim, dtype);
}

// aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mean_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::mean_out::call(self, dim, keepdim, dtype, out);
}
// aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::mean_out::call(self, dim, keepdim, dtype, out);
}

// aten::mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor mean(const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::mean_names_dim::call(self, dim, keepdim, dtype);
}

// aten::mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mean_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::mean_names_out::call(self, dim, keepdim, dtype, out);
}
// aten::mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mean_outf(const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::mean_names_out::call(self, dim, keepdim, dtype, out);
}

// aten::nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor nanmean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::nanmean::call(self, dim, keepdim, dtype);
}

// aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nanmean_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::nanmean_out::call(self, dim, keepdim, dtype, out);
}
// aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nanmean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::nanmean_out::call(self, dim, keepdim, dtype, out);
}

// aten::median(Tensor self) -> Tensor
inline at::Tensor median(const at::Tensor & self) {
    return at::_ops::median::call(self);
}

// aten::median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> median(const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::median_dim::call(self, dim, keepdim);
}

// aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> median_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::median_dim_values::call(self, dim, keepdim, values, indices);
}
// aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> median_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::median_dim_values::call(self, dim, keepdim, values, indices);
}

// aten::median.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> median(const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::median_names_dim::call(self, dim, keepdim);
}

// aten::median.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> median_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::median_names_dim_values::call(self, dim, keepdim, values, indices);
}
// aten::median.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> median_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::median_names_dim_values::call(self, dim, keepdim, values, indices);
}

// aten::nanmedian(Tensor self) -> Tensor
inline at::Tensor nanmedian(const at::Tensor & self) {
    return at::_ops::nanmedian::call(self);
}

// aten::nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> nanmedian(const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::nanmedian_dim::call(self, dim, keepdim);
}

// aten::nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> nanmedian_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::nanmedian_dim_values::call(self, dim, keepdim, values, indices);
}
// aten::nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> nanmedian_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::nanmedian_dim_values::call(self, dim, keepdim, values, indices);
}

// aten::nanmedian.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> nanmedian(const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::nanmedian_names_dim::call(self, dim, keepdim);
}

// aten::nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> nanmedian_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::nanmedian_names_dim_values::call(self, dim, keepdim, values, indices);
}
// aten::nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> nanmedian_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::nanmedian_names_dim_values::call(self, dim, keepdim, values, indices);
}

// aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> min(const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::min_dim::call(self, dim, keepdim);
}

// aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::min_dim_min::call(self, dim, keepdim, min, min_indices);
}
// aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> min_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) {
    return at::_ops::min_dim_min::call(self, dim, keepdim, min, min_indices);
}

// aten::min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> min(const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::min_names_dim::call(self, dim, keepdim);
}

// aten::min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> min_out(at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::min_names_dim_min::call(self, dim, keepdim, min, min_indices);
}
// aten::min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> min_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) {
    return at::_ops::min_names_dim_min::call(self, dim, keepdim, min, min_indices);
}

// aten::amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor
inline at::Tensor amin(const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false) {
    return at::_ops::amin::call(self, dim, keepdim);
}

// aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & amin_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false) {
    return at::_ops::amin_out::call(self, dim, keepdim, out);
}
// aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & amin_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::amin_out::call(self, dim, keepdim, out);
}

// aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor _mps_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_mps_convolution::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _mps_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_mps_convolution::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor _mps_convolution_symint(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_mps_convolution::call(self, weight, bias, padding, stride, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _mps_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_mps_convolution::call(self, weight, bias, padding, stride, dilation, groups);
  }
}

// aten::mps_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> mps_convolution_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::mps_convolution_backward::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> mps_convolution_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::mps_convolution_backward::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask);
  }
}

// aten::mps_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> mps_convolution_backward_symint(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::mps_convolution_backward::call(self, grad_output, weight, padding, stride, dilation, groups, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> mps_convolution_backward(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::mps_convolution_backward::call(self, grad_output, weight, padding, stride, dilation, groups, output_mask);
  }
}

// aten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor mkldnn_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::mkldnn_convolution::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor mkldnn_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::mkldnn_convolution::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor mkldnn_convolution_symint(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::mkldnn_convolution::call(self, weight, bias, padding, stride, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor mkldnn_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::mkldnn_convolution::call(self, weight, bias, padding, stride, dilation, groups);
  }
}

// aten::mkldnn_rnn_layer(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, int[] batch_sizes, int mode, int hidden_size, int num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> mkldnn_rnn_layer(const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) {
    return at::_ops::mkldnn_rnn_layer::call(input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train);
}

// aten::mkldnn_rnn_layer_backward(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> mkldnn_rnn_layer_backward(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) {
    return at::_ops::mkldnn_rnn_layer_backward::call(input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace);
}

// aten::miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> miopen_batch_norm(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon) {
    return at::_ops::miopen_batch_norm::call(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon);
}

// aten::miopen_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> miopen_batch_norm_backward(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon) {
    return at::_ops::miopen_batch_norm_backward::call(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon);
}

// aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor
inline at::Tensor miopen_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor miopen_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic);
  }
}

// aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor
inline at::Tensor miopen_convolution_symint(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor miopen_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic);
  }
}

// aten::miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor
inline at::Tensor miopen_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_transpose::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor miopen_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_transpose::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic);
  }
}

// aten::miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor
inline at::Tensor miopen_convolution_transpose_symint(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_transpose::call(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor miopen_convolution_transpose(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_transpose::call(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic);
  }
}

// aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor
inline at::Tensor miopen_depthwise_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_depthwise_convolution::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor miopen_depthwise_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_depthwise_convolution::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic);
  }
}

// aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor
inline at::Tensor miopen_depthwise_convolution_symint(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_depthwise_convolution::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor miopen_depthwise_convolution(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_depthwise_convolution::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic);
  }
}

// aten::miopen_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor miopen_convolution_relu(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::miopen_convolution_relu::call(self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor miopen_convolution_relu(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::miopen_convolution_relu::call(self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::miopen_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor miopen_convolution_relu_symint(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::miopen_convolution_relu::call(self, weight, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor miopen_convolution_relu(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::miopen_convolution_relu::call(self, weight, bias, stride, padding, dilation, groups);
  }
}

// aten::miopen_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor miopen_convolution_add_relu(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::miopen_convolution_add_relu::call(self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor miopen_convolution_add_relu(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::miopen_convolution_add_relu::call(self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups);
  }
}

// aten::miopen_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor
inline at::Tensor miopen_convolution_add_relu_symint(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::miopen_convolution_add_relu::call(self, weight, z, alpha, bias, stride, padding, dilation, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor miopen_convolution_add_relu(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::miopen_convolution_add_relu::call(self, weight, z, alpha, bias, stride, padding, dilation, groups);
  }
}

// aten::miopen_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> miopen_rnn(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::miopen_rnn::call(input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state);
}

// aten::miopen_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[])
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,::std::vector<at::Tensor>> miopen_rnn_backward(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::miopen_rnn_backward::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask);
}

// aten::mm(Tensor self, Tensor mat2) -> Tensor
inline at::Tensor mm(const at::Tensor & self, const at::Tensor & mat2) {
    return at::_ops::mm::call(self, mat2);
}

// aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) {
    return at::_ops::mm_out::call(self, mat2, out);
}
// aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mm_outf(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) {
    return at::_ops::mm_out::call(self, mat2, out);
}

// aten::_int_mm(Tensor self, Tensor mat2) -> Tensor
inline at::Tensor _int_mm(const at::Tensor & self, const at::Tensor & mat2) {
    return at::_ops::_int_mm::call(self, mat2);
}

// aten::_int_mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _int_mm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) {
    return at::_ops::_int_mm_out::call(self, mat2, out);
}
// aten::_int_mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _int_mm_outf(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) {
    return at::_ops::_int_mm_out::call(self, mat2, out);
}

// aten::_convert_weight_to_int4pack(Tensor self, int innerKTiles) -> Tensor
inline at::Tensor _convert_weight_to_int4pack(const at::Tensor & self, int64_t innerKTiles) {
    return at::_ops::_convert_weight_to_int4pack::call(self, innerKTiles);
}

// aten::_weight_int4pack_mm(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor
inline at::Tensor _weight_int4pack_mm(const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScaleAndZeros) {
    return at::_ops::_weight_int4pack_mm::call(self, mat2, qGroupSize, qScaleAndZeros);
}

// aten::_convert_weight_to_int4pack_for_cpu(Tensor self, int innerKTiles) -> Tensor
inline at::Tensor _convert_weight_to_int4pack_for_cpu(const at::Tensor & self, int64_t innerKTiles) {
    return at::_ops::_convert_weight_to_int4pack_for_cpu::call(self, innerKTiles);
}

// aten::_weight_int4pack_mm_for_cpu(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor
inline at::Tensor _weight_int4pack_mm_for_cpu(const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScaleAndZeros) {
    return at::_ops::_weight_int4pack_mm_for_cpu::call(self, mat2, qGroupSize, qScaleAndZeros);
}

// aten::_weight_int8pack_mm(Tensor self, Tensor mat2, Tensor scales) -> Tensor
inline at::Tensor _weight_int8pack_mm(const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scales) {
    return at::_ops::_weight_int8pack_mm::call(self, mat2, scales);
}

// aten::_sparse_mm(Tensor sparse, Tensor dense) -> Tensor
inline at::Tensor _sparse_mm(const at::Tensor & sparse, const at::Tensor & dense) {
    return at::_ops::_sparse_mm::call(sparse, dense);
}

// aten::_sparse_mm.reduce(Tensor sparse, Tensor dense, str reduce) -> Tensor
inline at::Tensor _sparse_mm(const at::Tensor & sparse, const at::Tensor & dense, c10::string_view reduce) {
    return at::_ops::_sparse_mm_reduce::call(sparse, dense, reduce);
}

// aten::_sparse_sparse_matmul(Tensor self, Tensor other) -> Tensor
inline at::Tensor _sparse_sparse_matmul(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::_sparse_sparse_matmul::call(self, other);
}

// aten::mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> mode(const at::Tensor & self, int64_t dim=-1, bool keepdim=false) {
    return at::_ops::mode::call(self, dim, keepdim);
}

// aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> mode_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim=-1, bool keepdim=false) {
    return at::_ops::mode_values::call(self, dim, keepdim, values, indices);
}
// aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> mode_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::mode_values::call(self, dim, keepdim, values, indices);
}

// aten::mode.dimname(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> mode(const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::mode_dimname::call(self, dim, keepdim);
}

// aten::mode.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> mode_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false) {
    return at::_ops::mode_dimname_out::call(self, dim, keepdim, values, indices);
}
// aten::mode.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> mode_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::mode_dimname_out::call(self, dim, keepdim, values, indices);
}

// aten::mul.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor mul(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::mul_Tensor::call(self, other);
}

// aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::mul_out::call(self, other, out);
}
// aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mul_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::mul_out::call(self, other, out);
}

// aten::mul.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor mul(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::mul_Scalar::call(self, other);
}

// aten::multiply.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor multiply(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::multiply_Tensor::call(self, other);
}

// aten::multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & multiply_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::multiply_out::call(self, other, out);
}
// aten::multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & multiply_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::multiply_out::call(self, other, out);
}

// aten::multiply.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor multiply(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::multiply_Scalar::call(self, other);
}

// aten::mv(Tensor self, Tensor vec) -> Tensor
inline at::Tensor mv(const at::Tensor & self, const at::Tensor & vec) {
    return at::_ops::mv::call(self, vec);
}

// aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mv_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & vec) {
    return at::_ops::mv_out::call(self, vec, out);
}
// aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mv_outf(const at::Tensor & self, const at::Tensor & vec, at::Tensor & out) {
    return at::_ops::mv_out::call(self, vec, out);
}

// aten::mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mvlgamma_out(at::Tensor & out, const at::Tensor & self, int64_t p) {
    return at::_ops::mvlgamma_out::call(self, p, out);
}
// aten::mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mvlgamma_outf(const at::Tensor & self, int64_t p, at::Tensor & out) {
    return at::_ops::mvlgamma_out::call(self, p, out);
}

// aten::mvlgamma(Tensor self, int p) -> Tensor
inline at::Tensor mvlgamma(const at::Tensor & self, int64_t p) {
    return at::_ops::mvlgamma::call(self, p);
}

// aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor
inline at::Tensor narrow_copy(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) {
    return at::_ops::narrow_copy::call(self, dim, start, length);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor narrow_copy(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) {
    return at::_ops::narrow_copy::call(self, dim, start, length);
  }
}

// aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor
inline at::Tensor narrow_copy_symint(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) {
    return at::_ops::narrow_copy::call(self, dim, start, length);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor narrow_copy(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) {
    return at::_ops::narrow_copy::call(self, dim, start, length);
  }
}

// aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & narrow_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, int64_t start, int64_t length) {
    return at::_ops::narrow_copy_out::call(self, dim, start, length, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & narrow_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, int64_t start, int64_t length) {
    return at::_ops::narrow_copy_out::call(self, dim, start, length, out);
  }
}

// aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & narrow_copy_outf(const at::Tensor & self, int64_t dim, int64_t start, int64_t length, at::Tensor & out) {
    return at::_ops::narrow_copy_out::call(self, dim, start, length, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & narrow_copy_outf(const at::Tensor & self, int64_t dim, int64_t start, int64_t length, at::Tensor & out) {
    return at::_ops::narrow_copy_out::call(self, dim, start, length, out);
  }
}

// aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & narrow_copy_symint_out(at::Tensor & out, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) {
    return at::_ops::narrow_copy_out::call(self, dim, start, length, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & narrow_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) {
    return at::_ops::narrow_copy_out::call(self, dim, start, length, out);
  }
}

// aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & narrow_copy_symint_outf(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out) {
    return at::_ops::narrow_copy_out::call(self, dim, start, length, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & narrow_copy_outf(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out) {
    return at::_ops::narrow_copy_out::call(self, dim, start, length, out);
  }
}

// aten::narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a)
inline at::Tensor narrow(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) {
    return at::_ops::narrow::call(self, dim, start, length);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor narrow(const at::Tensor & self, int64_t dim, int64_t start, int64_t length) {
    return at::_ops::narrow::call(self, dim, start, length);
  }
}

// aten::narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a)
inline at::Tensor narrow_symint(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) {
    return at::_ops::narrow::call(self, dim, start, length);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor narrow(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) {
    return at::_ops::narrow::call(self, dim, start, length);
  }
}

// aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a)
inline at::Tensor narrow(const at::Tensor & self, int64_t dim, const at::Tensor & start, int64_t length) {
    return at::_ops::narrow_Tensor::call(self, dim, start, length);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor narrow(const at::Tensor & self, int64_t dim, const at::Tensor & start, int64_t length) {
    return at::_ops::narrow_Tensor::call(self, dim, start, length);
  }
}

// aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a)
inline at::Tensor narrow_symint(const at::Tensor & self, int64_t dim, const at::Tensor & start, c10::SymInt length) {
    return at::_ops::narrow_Tensor::call(self, dim, start, length);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor narrow(const at::Tensor & self, int64_t dim, const at::Tensor & start, c10::SymInt length) {
    return at::_ops::narrow_Tensor::call(self, dim, start, length);
  }
}

// aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps) {
    return at::_ops::native_batch_norm::call(input, weight, bias, running_mean, running_var, training, momentum, eps);
}

// aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_out(at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps) {
    return at::_ops::native_batch_norm_out::call(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd);
}
// aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) {
    return at::_ops::native_batch_norm_out::call(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd);
}

// aten::_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps) {
    return at::_ops::_native_batch_norm_legit::call(input, weight, bias, running_mean, running_var, training, momentum, eps);
}

// aten::_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit_no_training(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) {
    return at::_ops::_native_batch_norm_legit_no_training::call(input, weight, bias, running_mean, running_var, momentum, eps);
}

// aten::_native_batch_norm_legit.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd) -> (Tensor(d!), Tensor(e!), Tensor(f!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_out(at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps) {
    return at::_ops::_native_batch_norm_legit_out::call(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd);
}
// aten::_native_batch_norm_legit.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd) -> (Tensor(d!), Tensor(e!), Tensor(f!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) {
    return at::_ops::_native_batch_norm_legit_out::call(input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd);
}

// aten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, bool training, double momentum, double eps) {
    return at::_ops::_native_batch_norm_legit_no_stats::call(input, weight, bias, training, momentum, eps);
}

// aten::_native_batch_norm_legit.no_stats_out(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_out(at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, bool training, double momentum, double eps) {
    return at::_ops::_native_batch_norm_legit_no_stats_out::call(input, weight, bias, training, momentum, eps, out, save_mean, save_invstd);
}
// aten::_native_batch_norm_legit.no_stats_out(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) {
    return at::_ops::_native_batch_norm_legit_no_stats_out::call(input, weight, bias, training, momentum, eps, out, save_mean, save_invstd);
}

// aten::batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> batch_norm_stats(const at::Tensor & input, double eps) {
    return at::_ops::batch_norm_stats::call(input, eps);
}

// aten::batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensor
inline at::Tensor batch_norm_elemt(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps) {
    return at::_ops::batch_norm_elemt::call(input, weight, bias, mean, invstd, eps);
}

// aten::batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & batch_norm_elemt_out(at::Tensor & out, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps) {
    return at::_ops::batch_norm_elemt_out::call(input, weight, bias, mean, invstd, eps, out);
}
// aten::batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & batch_norm_elemt_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps, at::Tensor & out) {
    return at::_ops::batch_norm_elemt_out::call(input, weight, bias, mean, invstd, eps, out);
}

// aten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> batch_norm_gather_stats(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count) {
    return at::_ops::batch_norm_gather_stats::call(input, mean, invstd, running_mean, running_var, momentum, eps, count);
}

// aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> batch_norm_gather_stats_with_counts(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts) {
    return at::_ops::batch_norm_gather_stats_with_counts::call(input, mean, invstd, running_mean, running_var, momentum, eps, counts);
}

// aten::native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> native_batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask) {
    return at::_ops::native_batch_norm_backward::call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask);
}

// aten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> batch_norm_backward_reduce(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g) {
    return at::_ops::batch_norm_backward_reduce::call(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g);
}

// aten::batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count) -> Tensor
inline at::Tensor batch_norm_backward_elemt(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count) {
    return at::_ops::batch_norm_backward_elemt::call(grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count);
}

// aten::batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> batch_norm_update_stats(const at::Tensor & input, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum) {
    return at::_ops::batch_norm_update_stats::call(input, running_mean, running_var, momentum);
}

// aten::is_vulkan_available() -> bool
inline bool is_vulkan_available() {
    return at::_ops::is_vulkan_available::call();
}

// aten::_nnpack_available() -> bool
inline bool _nnpack_available() {
    return at::_ops::_nnpack_available::call();
}

// aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1) -> Tensor
inline at::Tensor _nnpack_spatial_convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride=1) {
    return at::_ops::_nnpack_spatial_convolution::call(input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _nnpack_spatial_convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride=1) {
    return at::_ops::_nnpack_spatial_convolution::call(input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride));
  }
}

// aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1) -> Tensor
inline at::Tensor _nnpack_spatial_convolution_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride=c10::SymInt(1)) {
    return at::_ops::_nnpack_spatial_convolution::call(input, weight, bias, padding, stride);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _nnpack_spatial_convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride=c10::SymInt(1)) {
    return at::_ops::_nnpack_spatial_convolution::call(input, weight, bias, padding, stride);
  }
}

// aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor ones(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::ones_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor ones(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::ones_names::call(size, names, dtype, layout, device, pin_memory);
}

// aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor ones(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::ones::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor ones(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::ones::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor ones(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::ones::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor ones(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::ones::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor ones_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::ones::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor ones(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::ones::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor ones_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::ones::call(size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor ones(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::ones::call(size, dtype, layout, device, pin_memory);
  }
}

// aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ones_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::ones_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & ones_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::ones_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ones_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::ones_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & ones_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::ones_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ones_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::ones_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & ones_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::ones_out::call(size, out);
  }
}

// aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ones_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::ones_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & ones_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::ones_out::call(size, out);
  }
}

// aten::ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor ones_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::ones_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor ones_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::ones_like::call(self, dtype, layout, device, pin_memory, memory_format);
}

// aten::pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor
inline at::Tensor pairwise_distance(const at::Tensor & x1, const at::Tensor & x2, double p=2, double eps=1e-06, bool keepdim=false) {
    return at::_ops::pairwise_distance::call(x1, x2, p, eps, keepdim);
}

// aten::cdist(Tensor x1, Tensor x2, float p=2, int? compute_mode=None) -> Tensor
inline at::Tensor cdist(const at::Tensor & x1, const at::Tensor & x2, double p=2, ::std::optional<int64_t> compute_mode=::std::nullopt) {
    return at::_ops::cdist::call(x1, x2, p, compute_mode);
}

// aten::_euclidean_dist(Tensor x1, Tensor x2) -> Tensor
inline at::Tensor _euclidean_dist(const at::Tensor & x1, const at::Tensor & x2) {
    return at::_ops::_euclidean_dist::call(x1, x2);
}

// aten::_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor
inline at::Tensor _cdist_forward(const at::Tensor & x1, const at::Tensor & x2, double p, ::std::optional<int64_t> compute_mode) {
    return at::_ops::_cdist_forward::call(x1, x2, p, compute_mode);
}

// aten::_cdist_backward(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist) -> Tensor
inline at::Tensor _cdist_backward(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist) {
    return at::_ops::_cdist_backward::call(grad, x1, x2, p, cdist);
}

// aten::pdist(Tensor self, float p=2) -> Tensor
inline at::Tensor pdist(const at::Tensor & self, double p=2) {
    return at::_ops::pdist::call(self, p);
}

// aten::_pdist_forward(Tensor self, float p=2) -> Tensor
inline at::Tensor _pdist_forward(const at::Tensor & self, double p=2) {
    return at::_ops::_pdist_forward::call(self, p);
}

// aten::_pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor
inline at::Tensor _pdist_backward(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) {
    return at::_ops::_pdist_backward::call(grad, self, p, pdist);
}

// aten::cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor
inline at::Tensor cosine_similarity(const at::Tensor & x1, const at::Tensor & x2, int64_t dim=1, double eps=1e-08) {
    return at::_ops::cosine_similarity::call(x1, x2, dim, eps);
}

// aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)
inline at::Tensor permute(const at::Tensor & self, at::IntArrayRef dims) {
    return at::_ops::permute::call(self, dims);
}

// aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)
inline at::Tensor movedim(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) {
    return at::_ops::movedim_intlist::call(self, source, destination);
}

// aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a)
inline at::Tensor movedim(const at::Tensor & self, int64_t source, int64_t destination) {
    return at::_ops::movedim_int::call(self, source, destination);
}

// aten::moveaxis.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a)
inline at::Tensor moveaxis(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) {
    return at::_ops::moveaxis_intlist::call(self, source, destination);
}

// aten::moveaxis.int(Tensor(a) self, int source, int destination) -> Tensor(a)
inline at::Tensor moveaxis(const at::Tensor & self, int64_t source, int64_t destination) {
    return at::_ops::moveaxis_int::call(self, source, destination);
}

// aten::adjoint(Tensor(a) self) -> Tensor(a)
inline at::Tensor adjoint(const at::Tensor & self) {
    return at::_ops::adjoint::call(self);
}

// aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor
inline at::Tensor pixel_shuffle(const at::Tensor & self, int64_t upscale_factor) {
    return at::_ops::pixel_shuffle::call(self, upscale_factor);
}

// aten::pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor
inline at::Tensor pixel_unshuffle(const at::Tensor & self, int64_t downscale_factor) {
    return at::_ops::pixel_unshuffle::call(self, downscale_factor);
}

// aten::channel_shuffle(Tensor self, SymInt groups) -> Tensor
inline at::Tensor channel_shuffle(const at::Tensor & self, int64_t groups) {
    return at::_ops::channel_shuffle::call(self, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor channel_shuffle(const at::Tensor & self, int64_t groups) {
    return at::_ops::channel_shuffle::call(self, groups);
  }
}

// aten::channel_shuffle(Tensor self, SymInt groups) -> Tensor
inline at::Tensor channel_shuffle_symint(const at::Tensor & self, c10::SymInt groups) {
    return at::_ops::channel_shuffle::call(self, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor channel_shuffle(const at::Tensor & self, c10::SymInt groups) {
    return at::_ops::channel_shuffle::call(self, groups);
  }
}

// aten::native_channel_shuffle(Tensor self, SymInt groups) -> Tensor
inline at::Tensor native_channel_shuffle(const at::Tensor & self, int64_t groups) {
    return at::_ops::native_channel_shuffle::call(self, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor native_channel_shuffle(const at::Tensor & self, int64_t groups) {
    return at::_ops::native_channel_shuffle::call(self, groups);
  }
}

// aten::native_channel_shuffle(Tensor self, SymInt groups) -> Tensor
inline at::Tensor native_channel_shuffle_symint(const at::Tensor & self, c10::SymInt groups) {
    return at::_ops::native_channel_shuffle::call(self, groups);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor native_channel_shuffle(const at::Tensor & self, c10::SymInt groups) {
    return at::_ops::native_channel_shuffle::call(self, groups);
  }
}

// aten::_pin_memory(Tensor self, Device? device=None) -> Tensor
inline at::Tensor _pin_memory(const at::Tensor & self, ::std::optional<at::Device> device=::std::nullopt) {
    return at::_ops::_pin_memory::call(self, device);
}

// aten::pinverse(Tensor self, float rcond=1e-15) -> Tensor
inline at::Tensor pinverse(const at::Tensor & self, double rcond=1e-15) {
    return at::_ops::pinverse::call(self, rcond);
}

// aten::poisson_nll_loss(Tensor input, Tensor target, bool log_input, bool full, float eps, int reduction) -> Tensor
inline at::Tensor poisson_nll_loss(const at::Tensor & input, const at::Tensor & target, bool log_input, bool full, double eps, int64_t reduction) {
    return at::_ops::poisson_nll_loss::call(input, target, log_input, full, eps, reduction);
}

// aten::rad2deg(Tensor self) -> Tensor
inline at::Tensor rad2deg(const at::Tensor & self) {
    return at::_ops::rad2deg::call(self);
}

// aten::rad2deg_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & rad2deg_(at::Tensor & self) {
    return at::_ops::rad2deg_::call(self);
}

// aten::rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rad2deg_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::rad2deg_out::call(self, out);
}
// aten::rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rad2deg_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::rad2deg_out::call(self, out);
}

// aten::deg2rad(Tensor self) -> Tensor
inline at::Tensor deg2rad(const at::Tensor & self) {
    return at::_ops::deg2rad::call(self);
}

// aten::deg2rad_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & deg2rad_(at::Tensor & self) {
    return at::_ops::deg2rad_::call(self);
}

// aten::deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & deg2rad_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::deg2rad_out::call(self, out);
}
// aten::deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & deg2rad_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::deg2rad_out::call(self, out);
}

// aten::scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor scalar_tensor(const at::Scalar & s, at::TensorOptions options={}) {
    return at::_ops::scalar_tensor::call(s, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor scalar_tensor(const at::Scalar & s, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::scalar_tensor::call(s, dtype, layout, device, pin_memory);
}

// aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::rand_names::call(c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::rand_names::call(c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_names::call(c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_names::call(c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory);
  }
}

// aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::rand_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::rand_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_names::call(size, names, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_names::call(size, names, dtype, layout, device, pin_memory);
  }
}

// aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::rand_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::rand_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory);
  }
}

// aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::rand_generator_with_names::call(size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::rand_generator_with_names::call(size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_generator_with_names::call(size, generator, names, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_generator_with_names::call(size, generator, names, dtype, layout, device, pin_memory);
  }
}

// aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::rand::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rand(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::rand::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::rand::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rand(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::rand::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand::call(size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand::call(size, dtype, layout, device, pin_memory);
  }
}

// aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
    return at::_ops::rand_generator::call(c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
    return at::_ops::rand_generator::call(c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_generator::call(c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_generator::call(c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
  }
}

// aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
    return at::_ops::rand_generator::call(size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
    return at::_ops::rand_generator::call(size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_generator::call(size, generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::rand_generator::call(size, generator, dtype, layout, device, pin_memory);
  }
}

// aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::rand_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::rand_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::rand_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & rand_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::rand_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::rand_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & rand_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::rand_out::call(size, out);
  }
}

// aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::rand_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & rand_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::rand_out::call(size, out);
  }
}

// aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::rand_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::rand_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::rand_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::rand_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::rand_generator_out::call(size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & rand_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::rand_generator_out::call(size, generator, out);
  }
}

// aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::rand_generator_out::call(size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & rand_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::rand_generator_out::call(size, generator, out);
  }
}

// aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor rand_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::rand_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor rand_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::rand_like::call(self, dtype, layout, device, pin_memory, memory_format);
}

// aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint(int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) {
    return at::_ops::randint::call(high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint(int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) {
    return at::_ops::randint::call(high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint::call(high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint::call(high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) {
    return at::_ops::randint::call(high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) {
    return at::_ops::randint::call(high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint::call(high, size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint::call(high, size, dtype, layout, device, pin_memory);
  }
}

// aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_generator::call(high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_generator::call(high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_generator::call(high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_generator::call(high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
  }
}

// aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_generator::call(high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_generator::call(high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_generator::call(high, size, generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_generator::call(high, size, generator, dtype, layout, device, pin_memory);
  }
}

// aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_low::call(low, high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_low::call(low, high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_low::call(low, high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_low::call(low, high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_low::call(low, high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_low::call(low, high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_low::call(low, high, size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_low::call(low, high, size, dtype, layout, device, pin_memory);
  }
}

// aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_low_generator::call(low, high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_low_generator::call(low, high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_low_generator::call(low, high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_low_generator::call(low, high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
  }
}

// aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_low_generator::call(low, high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randint_low_generator::call(low, high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_low_generator::call(low, high, size, generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randint_low_generator::call(low, high, size, generator, dtype, layout, device, pin_memory);
  }
}

// aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size) {
    return at::_ops::randint_out::call(high, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size) {
    return at::_ops::randint_out::call(high, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::randint_out::call(high, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::randint_out::call(high, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size) {
    return at::_ops::randint_out::call(high, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size) {
    return at::_ops::randint_out::call(high, size, out);
  }
}

// aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_symint_outf(c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::randint_out::call(high, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_outf(c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::randint_out::call(high, size, out);
  }
}

// aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randint_generator_out::call(high, c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randint_generator_out::call(high, c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randint_generator_out::call(high, c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randint_generator_out::call(high, c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randint_generator_out::call(high, size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randint_generator_out::call(high, size, generator, out);
  }
}

// aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_symint_outf(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randint_generator_out::call(high, size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_outf(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randint_generator_out::call(high, size, generator, out);
  }
}

// aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size) {
    return at::_ops::randint_low_out::call(low, high, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size) {
    return at::_ops::randint_low_out::call(low, high, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::randint_low_out::call(low, high, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::randint_low_out::call(low, high, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size) {
    return at::_ops::randint_low_out::call(low, high, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size) {
    return at::_ops::randint_low_out::call(low, high, size, out);
  }
}

// aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_symint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::randint_low_out::call(low, high, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::randint_low_out::call(low, high, size, out);
  }
}

// aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randint_low_generator_out::call(low, high, c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randint_low_generator_out::call(low, high, c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randint_low_generator_out::call(low, high, c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randint_low_generator_out::call(low, high, c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randint_low_generator_out::call(low, high, size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randint_low_generator_out::call(low, high, size, generator, out);
  }
}

// aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_symint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randint_low_generator_out::call(low, high, size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randint_low_generator_out::call(low, high, size, generator, out);
  }
}

// aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randint_like(const at::Tensor & self, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint_like(const at::Tensor & self, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randint_like(const at::Tensor & self, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint_like(const at::Tensor & self, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format);
  }
}

// aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint_like(const at::Tensor & self, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint_like(const at::Tensor & self, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format);
  }
}

// aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_low_dtype::call(self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_low_dtype::call(self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format);
  }
}

// aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt low, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_low_dtype::call(self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint_like(const at::Tensor & self, c10::SymInt low, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_low_dtype::call(self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
  }
}

// aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randint_like(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format);
  }
}

// aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::randn::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randn(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::randn::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::randn::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randn(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::randn::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn::call(size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn::call(size, dtype, layout, device, pin_memory);
  }
}

// aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
    return at::_ops::randn_generator::call(c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
    return at::_ops::randn_generator::call(c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_generator::call(c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_generator::call(c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
  }
}

// aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
    return at::_ops::randn_generator::call(size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
    return at::_ops::randn_generator::call(size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_generator::call(size, generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_generator::call(size, generator, dtype, layout, device, pin_memory);
  }
}

// aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::randn_names::call(c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::randn_names::call(c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_names::call(c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_names::call(c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory);
  }
}

// aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::randn_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::randn_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_names::call(size, names, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_names::call(size, names, dtype, layout, device, pin_memory);
  }
}

// aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::randn_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::randn_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory);
  }
}

// aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::randn_generator_with_names::call(size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::randn_generator_with_names::call(size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_generator_with_names::call(size, generator, names, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randn_generator_with_names::call(size, generator, names, dtype, layout, device, pin_memory);
  }
}

// aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::randn_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::randn_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::randn_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randn_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::randn_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::randn_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randn_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::randn_out::call(size, out);
  }
}

// aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::randn_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randn_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::randn_out::call(size, out);
  }
}

// aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randn_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randn_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randn_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randn_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randn_generator_out::call(size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randn_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
    return at::_ops::randn_generator_out::call(size, generator, out);
  }
}

// aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randn_generator_out::call(size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randn_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randn_generator_out::call(size, generator, out);
  }
}

// aten::randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randn_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randn_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor randn_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::randn_like::call(self, dtype, layout, device, pin_memory, memory_format);
}

// aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randperm(int64_t n, at::TensorOptions options=at::kLong) {
    return at::_ops::randperm::call(n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randperm(int64_t n, at::TensorOptions options=at::kLong) {
    return at::_ops::randperm::call(n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randperm(int64_t n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randperm::call(n, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randperm(int64_t n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randperm::call(n, dtype, layout, device, pin_memory);
  }
}

// aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randperm_symint(c10::SymInt n, at::TensorOptions options=at::kLong) {
    return at::_ops::randperm::call(n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randperm(c10::SymInt n, at::TensorOptions options=at::kLong) {
    return at::_ops::randperm::call(n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randperm_symint(c10::SymInt n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randperm::call(n, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randperm(c10::SymInt n, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randperm::call(n, dtype, layout, device, pin_memory);
  }
}

// aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randperm(int64_t n, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randperm_generator::call(n, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randperm(int64_t n, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randperm_generator::call(n, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randperm(int64_t n, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randperm_generator::call(n, generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor randperm(int64_t n, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randperm_generator::call(n, generator, dtype, layout, device, pin_memory);
  }
}

// aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randperm_symint(c10::SymInt n, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randperm_generator::call(n, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randperm(c10::SymInt n, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
    return at::_ops::randperm_generator::call(n, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor randperm_symint(c10::SymInt n, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randperm_generator::call(n, generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor randperm(c10::SymInt n, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::randperm_generator::call(n, generator, dtype, layout, device, pin_memory);
  }
}

// aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randperm_out(at::Tensor & out, int64_t n) {
    return at::_ops::randperm_out::call(n, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randperm_out(at::Tensor & out, int64_t n) {
    return at::_ops::randperm_out::call(n, out);
  }
}

// aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randperm_outf(int64_t n, at::Tensor & out) {
    return at::_ops::randperm_out::call(n, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randperm_outf(int64_t n, at::Tensor & out) {
    return at::_ops::randperm_out::call(n, out);
  }
}

// aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randperm_symint_out(at::Tensor & out, c10::SymInt n) {
    return at::_ops::randperm_out::call(n, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randperm_out(at::Tensor & out, c10::SymInt n) {
    return at::_ops::randperm_out::call(n, out);
  }
}

// aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randperm_symint_outf(c10::SymInt n, at::Tensor & out) {
    return at::_ops::randperm_out::call(n, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randperm_outf(c10::SymInt n, at::Tensor & out) {
    return at::_ops::randperm_out::call(n, out);
  }
}

// aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randperm_out(at::Tensor & out, int64_t n, ::std::optional<at::Generator> generator) {
    return at::_ops::randperm_generator_out::call(n, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randperm_out(at::Tensor & out, int64_t n, ::std::optional<at::Generator> generator) {
    return at::_ops::randperm_generator_out::call(n, generator, out);
  }
}

// aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randperm_outf(int64_t n, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randperm_generator_out::call(n, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randperm_outf(int64_t n, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randperm_generator_out::call(n, generator, out);
  }
}

// aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randperm_symint_out(at::Tensor & out, c10::SymInt n, ::std::optional<at::Generator> generator) {
    return at::_ops::randperm_generator_out::call(n, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randperm_out(at::Tensor & out, c10::SymInt n, ::std::optional<at::Generator> generator) {
    return at::_ops::randperm_generator_out::call(n, generator, out);
  }
}

// aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randperm_symint_outf(c10::SymInt n, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randperm_generator_out::call(n, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randperm_outf(c10::SymInt n, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::randperm_generator_out::call(n, generator, out);
  }
}

// aten::range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor range(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step=1, at::TensorOptions options={}) {
    return at::_ops::range_step::call(start, end, step, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor range(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::range_step::call(start, end, step, dtype, layout, device, pin_memory);
}

// aten::range(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor range(const at::Scalar & start, const at::Scalar & end, at::TensorOptions options={}) {
    return at::_ops::range::call(start, end, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::range(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor range(const at::Scalar & start, const at::Scalar & end, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::range::call(start, end, dtype, layout, device, pin_memory);
}

// aten::range.out_(Scalar start, Scalar end, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & range_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end) {
    return at::_ops::range_out_::call(start, end, out);
}
// aten::range.out_(Scalar start, Scalar end, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & range_outf(const at::Scalar & start, const at::Scalar & end, at::Tensor & out) {
    return at::_ops::range_out_::call(start, end, out);
}

// aten::range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & range_out(at::Tensor & out, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step) {
    return at::_ops::range_out::call(start, end, step, out);
}
// aten::range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & range_outf(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) {
    return at::_ops::range_out::call(start, end, step, out);
}

// aten::ravel(Tensor(a) self) -> Tensor(a)
inline at::Tensor ravel(const at::Tensor & self) {
    return at::_ops::ravel::call(self);
}

// aten::reciprocal(Tensor self) -> Tensor
inline at::Tensor reciprocal(const at::Tensor & self) {
    return at::_ops::reciprocal::call(self);
}

// aten::reciprocal_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & reciprocal_(at::Tensor & self) {
    return at::_ops::reciprocal_::call(self);
}

// aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reciprocal_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::reciprocal_out::call(self, out);
}
// aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reciprocal_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::reciprocal_out::call(self, out);
}

// aten::neg(Tensor self) -> Tensor
inline at::Tensor neg(const at::Tensor & self) {
    return at::_ops::neg::call(self);
}

// aten::neg_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & neg_(at::Tensor & self) {
    return at::_ops::neg_::call(self);
}

// aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & neg_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::neg_out::call(self, out);
}
// aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & neg_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::neg_out::call(self, out);
}

// aten::negative(Tensor self) -> Tensor
inline at::Tensor negative(const at::Tensor & self) {
    return at::_ops::negative::call(self);
}

// aten::negative_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & negative_(at::Tensor & self) {
    return at::_ops::negative_::call(self);
}

// aten::negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & negative_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::negative_out::call(self, out);
}
// aten::negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & negative_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::negative_out::call(self, out);
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor repeat(const at::Tensor & self, at::IntArrayRef repeats) {
    return at::_ops::repeat::call(self, c10::fromIntArrayRefSlow(repeats));
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor repeat(const at::Tensor & self, c10::SymIntArrayRef repeats) {
    return at::_ops::repeat::call(self, repeats);
  }
}

// aten::repeat_interleave.Tensor(Tensor repeats, *, SymInt? output_size=None) -> Tensor
inline at::Tensor repeat_interleave(const at::Tensor & repeats, ::std::optional<int64_t> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_Tensor::call(repeats, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor repeat_interleave(const at::Tensor & repeats, ::std::optional<int64_t> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_Tensor::call(repeats, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt);
  }
}

// aten::repeat_interleave.Tensor(Tensor repeats, *, SymInt? output_size=None) -> Tensor
inline at::Tensor repeat_interleave_symint(const at::Tensor & repeats, ::std::optional<c10::SymInt> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_Tensor::call(repeats, output_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor repeat_interleave(const at::Tensor & repeats, ::std::optional<c10::SymInt> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_Tensor::call(repeats, output_size);
  }
}

// aten::repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor
inline at::Tensor repeat_interleave(const at::Tensor & self, const at::Tensor & repeats, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<int64_t> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_self_Tensor::call(self, repeats, dim, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor repeat_interleave(const at::Tensor & self, const at::Tensor & repeats, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<int64_t> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_self_Tensor::call(self, repeats, dim, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt);
  }
}

// aten::repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor
inline at::Tensor repeat_interleave_symint(const at::Tensor & self, const at::Tensor & repeats, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<c10::SymInt> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_self_Tensor::call(self, repeats, dim, output_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor repeat_interleave(const at::Tensor & self, const at::Tensor & repeats, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<c10::SymInt> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_self_Tensor::call(self, repeats, dim, output_size);
  }
}

// aten::repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor
inline at::Tensor repeat_interleave(const at::Tensor & self, int64_t repeats, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<int64_t> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_self_int::call(self, repeats, dim, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor repeat_interleave(const at::Tensor & self, int64_t repeats, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<int64_t> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_self_int::call(self, repeats, dim, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt);
  }
}

// aten::repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor
inline at::Tensor repeat_interleave_symint(const at::Tensor & self, c10::SymInt repeats, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<c10::SymInt> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_self_int::call(self, repeats, dim, output_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor repeat_interleave(const at::Tensor & self, c10::SymInt repeats, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<c10::SymInt> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_self_int::call(self, repeats, dim, output_size);
  }
}

// aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a)
inline at::Tensor reshape(const at::Tensor & self, at::IntArrayRef shape) {
    return at::_ops::reshape::call(self, c10::fromIntArrayRefSlow(shape));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor reshape(const at::Tensor & self, at::IntArrayRef shape) {
    return at::_ops::reshape::call(self, c10::fromIntArrayRefSlow(shape));
  }
}

// aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a)
inline at::Tensor reshape_symint(const at::Tensor & self, c10::SymIntArrayRef shape) {
    return at::_ops::reshape::call(self, shape);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor reshape(const at::Tensor & self, c10::SymIntArrayRef shape) {
    return at::_ops::reshape::call(self, shape);
  }
}

// aten::_reshape_copy(Tensor self, SymInt[] size) -> Tensor
inline at::Tensor _reshape_copy(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_reshape_copy::call(self, c10::fromIntArrayRefSlow(size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _reshape_copy(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_reshape_copy::call(self, c10::fromIntArrayRefSlow(size));
  }
}

// aten::_reshape_copy(Tensor self, SymInt[] size) -> Tensor
inline at::Tensor _reshape_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::_reshape_copy::call(self, size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _reshape_copy(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::_reshape_copy::call(self, size);
  }
}

// aten::_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a)
inline at::Tensor _reshape_alias(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::_reshape_alias::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _reshape_alias(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::_reshape_alias::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
  }
}

// aten::_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a)
inline at::Tensor _reshape_alias_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::_reshape_alias::call(self, size, stride);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _reshape_alias(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::_reshape_alias::call(self, size, stride);
  }
}

// aten::_mkldnn_reshape(Tensor self, int[] shape) -> Tensor
inline at::Tensor _mkldnn_reshape(const at::Tensor & self, at::IntArrayRef shape) {
    return at::_ops::_mkldnn_reshape::call(self, shape);
}

// aten::round(Tensor self) -> Tensor
inline at::Tensor round(const at::Tensor & self) {
    return at::_ops::round::call(self);
}

// aten::round_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & round_(at::Tensor & self) {
    return at::_ops::round_::call(self);
}

// aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & round_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::round_out::call(self, out);
}
// aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & round_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::round_out::call(self, out);
}

// aten::round.decimals(Tensor self, *, int decimals) -> Tensor
inline at::Tensor round(const at::Tensor & self, int64_t decimals) {
    return at::_ops::round_decimals::call(self, decimals);
}

// aten::round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!)
inline at::Tensor & round_(at::Tensor & self, int64_t decimals) {
    return at::_ops::round__decimals::call(self, decimals);
}

// aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & round_out(at::Tensor & out, const at::Tensor & self, int64_t decimals) {
    return at::_ops::round_decimals_out::call(self, decimals, out);
}
// aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & round_outf(const at::Tensor & self, int64_t decimals, at::Tensor & out) {
    return at::_ops::round_decimals_out::call(self, decimals, out);
}

// aten::rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor
inline at::Tensor rrelu(const at::Tensor & self, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::rrelu::call(self, lower, upper, training, generator);
}

// aten::rrelu_(Tensor(a!) self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)
inline at::Tensor & rrelu_(at::Tensor & self, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::rrelu_::call(self, lower, upper, training, generator);
}

// aten::relu(Tensor self) -> Tensor
inline at::Tensor relu(const at::Tensor & self) {
    return at::_ops::relu::call(self);
}

// aten::relu_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & relu_(at::Tensor & self) {
    return at::_ops::relu_::call(self);
}

// aten::relu6(Tensor self) -> Tensor
inline at::Tensor relu6(const at::Tensor & self) {
    return at::_ops::relu6::call(self);
}

// aten::relu6_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & relu6_(at::Tensor & self) {
    return at::_ops::relu6_::call(self);
}

// aten::prelu(Tensor self, Tensor weight) -> Tensor
inline at::Tensor prelu(const at::Tensor & self, const at::Tensor & weight) {
    return at::_ops::prelu::call(self, weight);
}

// aten::_prelu_kernel(Tensor self, Tensor weight) -> Tensor
inline at::Tensor _prelu_kernel(const at::Tensor & self, const at::Tensor & weight) {
    return at::_ops::_prelu_kernel::call(self, weight);
}

// aten::_prelu_kernel_backward(Tensor grad_output, Tensor self, Tensor weight) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _prelu_kernel_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight) {
    return at::_ops::_prelu_kernel_backward::call(grad_output, self, weight);
}

// aten::gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gelu_out(at::Tensor & out, const at::Tensor & self, c10::string_view approximate="none") {
    return at::_ops::gelu_out::call(self, approximate, out);
}
// aten::gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gelu_outf(const at::Tensor & self, c10::string_view approximate, at::Tensor & out) {
    return at::_ops::gelu_out::call(self, approximate, out);
}

// aten::gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!)
inline at::Tensor & gelu_(at::Tensor & self, c10::string_view approximate="none") {
    return at::_ops::gelu_::call(self, approximate);
}

// aten::gelu(Tensor self, *, str approximate='none') -> Tensor
inline at::Tensor gelu(const at::Tensor & self, c10::string_view approximate="none") {
    return at::_ops::gelu::call(self, approximate);
}

// aten::gelu_backward.grad_input(Tensor grad_output, Tensor self, *, str approximate='none', Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & gelu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate="none") {
    return at::_ops::gelu_backward_grad_input::call(grad_output, self, approximate, grad_input);
}
// aten::gelu_backward.grad_input(Tensor grad_output, Tensor self, *, str approximate='none', Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & gelu_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate, at::Tensor & grad_input) {
    return at::_ops::gelu_backward_grad_input::call(grad_output, self, approximate, grad_input);
}

// aten::gelu_backward(Tensor grad_output, Tensor self, *, str approximate='none') -> Tensor
inline at::Tensor gelu_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate="none") {
    return at::_ops::gelu_backward::call(grad_output, self, approximate);
}

// aten::infinitely_differentiable_gelu_backward(Tensor grad, Tensor self) -> Tensor
inline at::Tensor infinitely_differentiable_gelu_backward(const at::Tensor & grad, const at::Tensor & self) {
    return at::_ops::infinitely_differentiable_gelu_backward::call(grad, self);
}

// aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardshrink_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & lambd=0.5) {
    return at::_ops::hardshrink_out::call(self, lambd, out);
}
// aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardshrink_outf(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) {
    return at::_ops::hardshrink_out::call(self, lambd, out);
}

// aten::hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor
inline at::Tensor hardshrink(const at::Tensor & self, const at::Scalar & lambd=0.5) {
    return at::_ops::hardshrink::call(self, lambd);
}

// aten::hardshrink_backward.grad_input(Tensor grad_out, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & hardshrink_backward_out(at::Tensor & grad_input, const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) {
    return at::_ops::hardshrink_backward_grad_input::call(grad_out, self, lambd, grad_input);
}
// aten::hardshrink_backward.grad_input(Tensor grad_out, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & hardshrink_backward_outf(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) {
    return at::_ops::hardshrink_backward_grad_input::call(grad_out, self, lambd, grad_input);
}

// aten::hardshrink_backward(Tensor grad_out, Tensor self, Scalar lambd) -> Tensor
inline at::Tensor hardshrink_backward(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) {
    return at::_ops::hardshrink_backward::call(grad_out, self, lambd);
}

// aten::rsqrt(Tensor self) -> Tensor
inline at::Tensor rsqrt(const at::Tensor & self) {
    return at::_ops::rsqrt::call(self);
}

// aten::rsqrt_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & rsqrt_(at::Tensor & self) {
    return at::_ops::rsqrt_::call(self);
}

// aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rsqrt_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::rsqrt_out::call(self, out);
}
// aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rsqrt_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::rsqrt_out::call(self, out);
}

// aten::select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a)
inline at::Tensor select(const at::Tensor & self, at::Dimname dim, int64_t index) {
    return at::_ops::select_Dimname::call(self, dim, index);
}

// aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a)
inline at::Tensor select(const at::Tensor & self, int64_t dim, int64_t index) {
    return at::_ops::select_int::call(self, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor select(const at::Tensor & self, int64_t dim, int64_t index) {
    return at::_ops::select_int::call(self, dim, index);
  }
}

// aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a)
inline at::Tensor select_symint(const at::Tensor & self, int64_t dim, c10::SymInt index) {
    return at::_ops::select_int::call(self, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor select(const at::Tensor & self, int64_t dim, c10::SymInt index) {
    return at::_ops::select_int::call(self, dim, index);
  }
}

// aten::select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor
inline at::Tensor select_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index) {
    return at::_ops::select_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor select_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index) {
    return at::_ops::select_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index);
  }
}

// aten::select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor
inline at::Tensor select_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) {
    return at::_ops::select_backward::call(grad_output, input_sizes, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor select_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) {
    return at::_ops::select_backward::call(grad_output, input_sizes, dim, index);
  }
}

// aten::_nested_select_backward(Tensor grad_output, Tensor self, int dim, SymInt index) -> Tensor
inline at::Tensor _nested_select_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, int64_t index) {
    return at::_ops::_nested_select_backward::call(grad_output, self, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _nested_select_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, int64_t index) {
    return at::_ops::_nested_select_backward::call(grad_output, self, dim, index);
  }
}

// aten::_nested_select_backward(Tensor grad_output, Tensor self, int dim, SymInt index) -> Tensor
inline at::Tensor _nested_select_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, c10::SymInt index) {
    return at::_ops::_nested_select_backward::call(grad_output, self, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _nested_select_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, c10::SymInt index) {
    return at::_ops::_nested_select_backward::call(grad_output, self, dim, index);
  }
}

// aten::selu(Tensor self) -> Tensor
inline at::Tensor selu(const at::Tensor & self) {
    return at::_ops::selu::call(self);
}

// aten::selu_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & selu_(at::Tensor & self) {
    return at::_ops::selu_::call(self);
}

// aten::celu(Tensor self, Scalar alpha=1.0) -> Tensor
inline at::Tensor celu(const at::Tensor & self, const at::Scalar & alpha=1.0) {
    return at::_ops::celu::call(self, alpha);
}

// aten::celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!)
inline at::Tensor & celu_(at::Tensor & self, const at::Scalar & alpha=1.0) {
    return at::_ops::celu_::call(self, alpha);
}

// aten::silu(Tensor self) -> Tensor
inline at::Tensor silu(const at::Tensor & self) {
    return at::_ops::silu::call(self);
}

// aten::silu_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & silu_(at::Tensor & self) {
    return at::_ops::silu_::call(self);
}

// aten::silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & silu_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::silu_out::call(self, out);
}
// aten::silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & silu_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::silu_out::call(self, out);
}

// aten::silu_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & silu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::silu_backward_grad_input::call(grad_output, self, grad_input);
}
// aten::silu_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & silu_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) {
    return at::_ops::silu_backward_grad_input::call(grad_output, self, grad_input);
}

// aten::silu_backward(Tensor grad_output, Tensor self) -> Tensor
inline at::Tensor silu_backward(const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::silu_backward::call(grad_output, self);
}

// aten::mish(Tensor self) -> Tensor
inline at::Tensor mish(const at::Tensor & self) {
    return at::_ops::mish::call(self);
}

// aten::mish_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & mish_(at::Tensor & self) {
    return at::_ops::mish_::call(self);
}

// aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mish_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::mish_out::call(self, out);
}
// aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mish_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::mish_out::call(self, out);
}

// aten::mish_backward(Tensor grad_output, Tensor self) -> Tensor
inline at::Tensor mish_backward(const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::mish_backward::call(grad_output, self);
}

// aten::sigmoid(Tensor self) -> Tensor
inline at::Tensor sigmoid(const at::Tensor & self) {
    return at::_ops::sigmoid::call(self);
}

// aten::sigmoid_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & sigmoid_(at::Tensor & self) {
    return at::_ops::sigmoid_::call(self);
}

// aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sigmoid_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::sigmoid_out::call(self, out);
}
// aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sigmoid_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::sigmoid_out::call(self, out);
}

// aten::logit(Tensor self, float? eps=None) -> Tensor
inline at::Tensor logit(const at::Tensor & self, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::logit::call(self, eps);
}

// aten::logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!)
inline at::Tensor & logit_(at::Tensor & self, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::logit_::call(self, eps);
}

// aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logit_out(at::Tensor & out, const at::Tensor & self, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::logit_out::call(self, eps, out);
}
// aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & logit_outf(const at::Tensor & self, ::std::optional<double> eps, at::Tensor & out) {
    return at::_ops::logit_out::call(self, eps, out);
}

// aten::sin(Tensor self) -> Tensor
inline at::Tensor sin(const at::Tensor & self) {
    return at::_ops::sin::call(self);
}

// aten::sin_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & sin_(at::Tensor & self) {
    return at::_ops::sin_::call(self);
}

// aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sin_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::sin_out::call(self, out);
}
// aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sin_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::sin_out::call(self, out);
}

// aten::sinc(Tensor self) -> Tensor
inline at::Tensor sinc(const at::Tensor & self) {
    return at::_ops::sinc::call(self);
}

// aten::sinc_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & sinc_(at::Tensor & self) {
    return at::_ops::sinc_::call(self);
}

// aten::sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sinc_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::sinc_out::call(self, out);
}
// aten::sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sinc_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::sinc_out::call(self, out);
}

// aten::sinh(Tensor self) -> Tensor
inline at::Tensor sinh(const at::Tensor & self) {
    return at::_ops::sinh::call(self);
}

// aten::sinh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & sinh_(at::Tensor & self) {
    return at::_ops::sinh_::call(self);
}

// aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sinh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::sinh_out::call(self, out);
}
// aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sinh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::sinh_out::call(self, out);
}

// aten::detach(Tensor(a) self) -> Tensor(a)
inline at::Tensor detach(const at::Tensor & self) {
    return at::_ops::detach::call(self);
}

// aten::detach_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & detach_(at::Tensor & self) {
    return at::_ops::detach_::call(self);
}

// aten::size.int(Tensor self, int dim) -> int
inline int64_t __dispatch_size(const at::Tensor & self, int64_t dim) {
    return at::_ops::size_int::call(self, dim);
}

// aten::size.Dimname(Tensor self, Dimname dim) -> int
inline int64_t size(const at::Tensor & self, at::Dimname dim) {
    return at::_ops::size_Dimname::call(self, dim);
}

// aten::sym_size.int(Tensor self, int dim) -> SymInt
inline c10::SymInt __dispatch_sym_size(const at::Tensor & self, int64_t dim) {
    return at::_ops::sym_size_int::call(self, dim);
}

// aten::sym_numel(Tensor self) -> SymInt
inline c10::SymInt __dispatch_sym_numel(const at::Tensor & self) {
    return at::_ops::sym_numel::call(self);
}

// aten::sym_storage_offset(Tensor self) -> SymInt
inline c10::SymInt __dispatch_sym_storage_offset(const at::Tensor & self) {
    return at::_ops::sym_storage_offset::call(self);
}

// aten::slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)
inline at::Tensor slice(const at::Tensor & self, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_Tensor::call(self, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slice(const at::Tensor & self, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_Tensor::call(self, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step);
  }
}

// aten::slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)
inline at::Tensor slice_symint(const at::Tensor & self, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_Tensor::call(self, dim, start, end, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slice(const at::Tensor & self, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_Tensor::call(self, dim, start, end, step);
  }
}

// aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor
inline at::Tensor slice_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
    return at::_ops::slice_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slice_backward(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
    return at::_ops::slice_backward::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step);
  }
}

// aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor
inline at::Tensor slice_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
    return at::_ops::slice_backward::call(grad_output, input_sizes, dim, start, end, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slice_backward(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
    return at::_ops::slice_backward::call(grad_output, input_sizes, dim, start, end, step);
  }
}

// aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)
inline at::Tensor slice_inverse(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_inverse::call(self, src, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slice_inverse(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_inverse::call(self, src, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step);
  }
}

// aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)
inline at::Tensor slice_inverse_symint(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_inverse::call(self, src, dim, start, end, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slice_inverse(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_inverse::call(self, src, dim, start, end, step);
  }
}

// aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor
inline at::Tensor slice_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_scatter::call(self, src, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slice_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_scatter::call(self, src, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step);
  }
}

// aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor
inline at::Tensor slice_scatter_symint(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_scatter::call(self, src, dim, start, end, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slice_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_scatter::call(self, src, dim, start, end, step);
  }
}

// aten::select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor
inline at::Tensor select_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index) {
    return at::_ops::select_scatter::call(self, src, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor select_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index) {
    return at::_ops::select_scatter::call(self, src, dim, index);
  }
}

// aten::select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor
inline at::Tensor select_scatter_symint(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index) {
    return at::_ops::select_scatter::call(self, src, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor select_scatter(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index) {
    return at::_ops::select_scatter::call(self, src, dim, index);
  }
}

// aten::diagonal_scatter(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1) -> Tensor
inline at::Tensor diagonal_scatter(const at::Tensor & self, const at::Tensor & src, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) {
    return at::_ops::diagonal_scatter::call(self, src, offset, dim1, dim2);
}

// aten::as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor
inline at::Tensor as_strided_scatter(const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_scatter::call(self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor as_strided_scatter(const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_scatter::call(self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt);
  }
}

// aten::as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor
inline at::Tensor as_strided_scatter_symint(const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_scatter::call(self, src, size, stride, storage_offset);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor as_strided_scatter(const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_scatter::call(self, src, size, stride, storage_offset);
  }
}

// aten::smm(Tensor self, Tensor mat2) -> Tensor
inline at::Tensor smm(const at::Tensor & self, const at::Tensor & mat2) {
    return at::_ops::smm::call(self, mat2);
}

// aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
inline at::Tensor softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::softmax_int::call(self, dim, dtype);
}

// aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::softmax_int_out::call(self, dim, dtype, out);
}
// aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & softmax_outf(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::softmax_int_out::call(self, dim, dtype, out);
}

// aten::softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor softmax(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::softmax_Dimname::call(self, dim, dtype);
}

// aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
inline at::Tensor _softmax(const at::Tensor & self, int64_t dim, bool half_to_float) {
    return at::_ops::_softmax::call(self, dim, half_to_float);
}

// aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) {
    return at::_ops::_softmax_out::call(self, dim, half_to_float, out);
}
// aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _softmax_outf(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) {
    return at::_ops::_softmax_out::call(self, dim, half_to_float, out);
}

// aten::_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor
inline at::Tensor _softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) {
    return at::_ops::_softmax_backward_data::call(grad_output, output, dim, input_dtype);
}

// aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _softmax_backward_data_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) {
    return at::_ops::_softmax_backward_data_out::call(grad_output, output, dim, input_dtype, grad_input);
}
// aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _softmax_backward_data_outf(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & grad_input) {
    return at::_ops::_softmax_backward_data_out::call(grad_output, output, dim, input_dtype, grad_input);
}

// aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::unsafe_split_Tensor::call(self, split_size, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::unsafe_split_Tensor::call(self, split_size, dim);
  }
}

// aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> unsafe_split_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::unsafe_split_Tensor::call(self, split_size, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::unsafe_split_Tensor::call(self, split_size, dim);
  }
}

// aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> split(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::split_Tensor::call(self, split_size, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> split(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::split_Tensor::call(self, split_size, dim);
  }
}

// aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> split_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::split_Tensor::call(self, split_size, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> split(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::split_Tensor::call(self, split_size, dim);
  }
}

// aten::split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> split(const at::Tensor & self, at::IntArrayRef split_size, int64_t dim=0) {
    return at::_ops::split_sizes::call(self, c10::fromIntArrayRefSlow(split_size), dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> split(const at::Tensor & self, at::IntArrayRef split_size, int64_t dim=0) {
    return at::_ops::split_sizes::call(self, c10::fromIntArrayRefSlow(split_size), dim);
  }
}

// aten::split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> split_symint(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim=0) {
    return at::_ops::split_sizes::call(self, split_size, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> split(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim=0) {
    return at::_ops::split_sizes::call(self, split_size, dim);
  }
}

// aten::unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> unsafe_split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::unsafe_split_with_sizes::call(self, c10::fromIntArrayRefSlow(split_sizes), dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> unsafe_split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::unsafe_split_with_sizes::call(self, c10::fromIntArrayRefSlow(split_sizes), dim);
  }
}

// aten::unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> unsafe_split_with_sizes_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::unsafe_split_with_sizes::call(self, split_sizes, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> unsafe_split_with_sizes(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::unsafe_split_with_sizes::call(self, split_sizes, dim);
  }
}

// aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes::call(self, c10::fromIntArrayRefSlow(split_sizes), dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes::call(self, c10::fromIntArrayRefSlow(split_sizes), dim);
  }
}

// aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> split_with_sizes_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes::call(self, split_sizes, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes::call(self, split_sizes, dim);
  }
}

// aten::hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]
inline ::std::vector<at::Tensor> hsplit(const at::Tensor & self, int64_t sections) {
    return at::_ops::hsplit_int::call(self, sections);
}

// aten::hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]
inline ::std::vector<at::Tensor> hsplit(const at::Tensor & self, at::IntArrayRef indices) {
    return at::_ops::hsplit_array::call(self, indices);
}

// aten::vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]
inline ::std::vector<at::Tensor> vsplit(const at::Tensor & self, int64_t sections) {
    return at::_ops::vsplit_int::call(self, sections);
}

// aten::vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]
inline ::std::vector<at::Tensor> vsplit(const at::Tensor & self, at::IntArrayRef indices) {
    return at::_ops::vsplit_array::call(self, indices);
}

// aten::dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]
inline ::std::vector<at::Tensor> dsplit(const at::Tensor & self, int64_t sections) {
    return at::_ops::dsplit_int::call(self, sections);
}

// aten::dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]
inline ::std::vector<at::Tensor> dsplit(const at::Tensor & self, at::IntArrayRef indices) {
    return at::_ops::dsplit_array::call(self, indices);
}

// aten::squeeze(Tensor(a) self) -> Tensor(a)
inline at::Tensor squeeze(const at::Tensor & self) {
    return at::_ops::squeeze::call(self);
}

// aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)
inline at::Tensor squeeze(const at::Tensor & self, int64_t dim) {
    return at::_ops::squeeze_dim::call(self, dim);
}

// aten::squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a)
inline at::Tensor squeeze(const at::Tensor & self, at::Dimname dim) {
    return at::_ops::squeeze_dimname::call(self, dim);
}

// aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)
inline at::Tensor squeeze(const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::squeeze_dims::call(self, dim);
}

// aten::sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inline at::Tensor sspaddmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::sspaddmm::call(self, mat1, mat2, beta, alpha);
}

// aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sspaddmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::sspaddmm_out::call(self, mat1, mat2, beta, alpha, out);
}
// aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sspaddmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::sspaddmm_out::call(self, mat1, mat2, beta, alpha, out);
}

// aten::_chunk_cat(Tensor[] tensors, int dim, int num_chunks) -> Tensor
inline at::Tensor _chunk_cat(at::TensorList tensors, int64_t dim, int64_t num_chunks) {
    return at::_ops::_chunk_cat::call(tensors, dim, num_chunks);
}

// aten::_chunk_cat.out(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _chunk_cat_out(at::Tensor & out, at::TensorList tensors, int64_t dim, int64_t num_chunks) {
    return at::_ops::_chunk_cat_out::call(tensors, dim, num_chunks, out);
}
// aten::_chunk_cat.out(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _chunk_cat_outf(at::TensorList tensors, int64_t dim, int64_t num_chunks, at::Tensor & out) {
    return at::_ops::_chunk_cat_out::call(tensors, dim, num_chunks, out);
}

// aten::stack(Tensor[] tensors, int dim=0) -> Tensor
inline at::Tensor stack(at::TensorList tensors, int64_t dim=0) {
    return at::_ops::stack::call(tensors, dim);
}

// aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & stack_out(at::Tensor & out, at::TensorList tensors, int64_t dim=0) {
    return at::_ops::stack_out::call(tensors, dim, out);
}
// aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & stack_outf(at::TensorList tensors, int64_t dim, at::Tensor & out) {
    return at::_ops::stack_out::call(tensors, dim, out);
}

// aten::_stack(Tensor[] tensors, int dim=0) -> Tensor
inline at::Tensor _stack(at::TensorList tensors, int64_t dim=0) {
    return at::_ops::_stack::call(tensors, dim);
}

// aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _stack_out(at::Tensor & out, at::TensorList tensors, int64_t dim=0) {
    return at::_ops::_stack_out::call(tensors, dim, out);
}
// aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _stack_outf(at::TensorList tensors, int64_t dim, at::Tensor & out) {
    return at::_ops::_stack_out::call(tensors, dim, out);
}

// aten::hstack(Tensor[] tensors) -> Tensor
inline at::Tensor hstack(at::TensorList tensors) {
    return at::_ops::hstack::call(tensors);
}

// aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hstack_out(at::Tensor & out, at::TensorList tensors) {
    return at::_ops::hstack_out::call(tensors, out);
}
// aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hstack_outf(at::TensorList tensors, at::Tensor & out) {
    return at::_ops::hstack_out::call(tensors, out);
}

// aten::vstack(Tensor[] tensors) -> Tensor
inline at::Tensor vstack(at::TensorList tensors) {
    return at::_ops::vstack::call(tensors);
}

// aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & vstack_out(at::Tensor & out, at::TensorList tensors) {
    return at::_ops::vstack_out::call(tensors, out);
}
// aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & vstack_outf(at::TensorList tensors, at::Tensor & out) {
    return at::_ops::vstack_out::call(tensors, out);
}

// aten::dstack(Tensor[] tensors) -> Tensor
inline at::Tensor dstack(at::TensorList tensors) {
    return at::_ops::dstack::call(tensors);
}

// aten::dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & dstack_out(at::Tensor & out, at::TensorList tensors) {
    return at::_ops::dstack_out::call(tensors, out);
}
// aten::dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & dstack_outf(at::TensorList tensors, at::Tensor & out) {
    return at::_ops::dstack_out::call(tensors, out);
}

// aten::stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor
inline at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool normalized, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<bool> return_complex=::std::nullopt) {
    return at::_ops::stft::call(self, n_fft, hop_length, win_length, window, normalized, onesided, return_complex);
}

// aten::stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor
inline at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length=::std::nullopt, ::std::optional<int64_t> win_length=::std::nullopt, const ::std::optional<at::Tensor> & window={}, bool center=true, c10::string_view pad_mode="reflect", bool normalized=false, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<bool> return_complex=::std::nullopt) {
    return at::_ops::stft_center::call(self, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex);
}

// aten::istft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, bool normalized=False, bool? onesided=None, int? length=None, bool return_complex=False) -> Tensor
inline at::Tensor istft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length=::std::nullopt, ::std::optional<int64_t> win_length=::std::nullopt, const ::std::optional<at::Tensor> & window={}, bool center=true, bool normalized=false, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<int64_t> length=::std::nullopt, bool return_complex=false) {
    return at::_ops::istft::call(self, n_fft, hop_length, win_length, window, center, normalized, onesided, length, return_complex);
}

// aten::stride.int(Tensor self, int dim) -> int
inline int64_t __dispatch_stride(const at::Tensor & self, int64_t dim) {
    return at::_ops::stride_int::call(self, dim);
}

// aten::stride.Dimname(Tensor self, Dimname dim) -> int
inline int64_t stride(const at::Tensor & self, at::Dimname dim) {
    return at::_ops::stride_Dimname::call(self, dim);
}

// aten::sym_stride.int(Tensor self, int dim) -> SymInt
inline c10::SymInt __dispatch_sym_stride(const at::Tensor & self, int64_t dim) {
    return at::_ops::sym_stride_int::call(self, dim);
}

// aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor sum(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::sum::call(self, dtype);
}

// aten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::sum_dim_IntList::call(self, dim, keepdim, dtype);
}

// aten::sum.dim_DimnameList(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor sum(const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::sum_dim_DimnameList::call(self, dim, keepdim, dtype);
}

// aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::sum_IntList_out::call(self, dim, keepdim, dtype, out);
}
// aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sum_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::sum_IntList_out::call(self, dim, keepdim, dtype, out);
}

// aten::sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::sum_DimnameList_out::call(self, dim, keepdim, dtype, out);
}
// aten::sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sum_outf(const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::sum_DimnameList_out::call(self, dim, keepdim, dtype, out);
}

// aten::_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor
inline at::Tensor _nested_sum_backward(const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) {
    return at::_ops::_nested_sum_backward::call(grad, self, dim, keepdim);
}

// aten::nansum(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor nansum(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::nansum::call(self, dim, keepdim, dtype);
}

// aten::nansum.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nansum_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::nansum_out::call(self, dim, keepdim, dtype, out);
}
// aten::nansum.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nansum_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::nansum_out::call(self, dim, keepdim, dtype, out);
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor sum_to_size(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::sum_to_size::call(self, c10::fromIntArrayRefSlow(size));
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor sum_to_size(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::sum_to_size::call(self, size);
  }
}

// aten::sqrt(Tensor self) -> Tensor
inline at::Tensor sqrt(const at::Tensor & self) {
    return at::_ops::sqrt::call(self);
}

// aten::sqrt_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & sqrt_(at::Tensor & self) {
    return at::_ops::sqrt_::call(self);
}

// aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sqrt_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::sqrt_out::call(self, out);
}
// aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sqrt_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::sqrt_out::call(self, out);
}

// aten::square(Tensor self) -> Tensor
inline at::Tensor square(const at::Tensor & self) {
    return at::_ops::square::call(self);
}

// aten::square_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & square_(at::Tensor & self) {
    return at::_ops::square_::call(self);
}

// aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & square_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::square_out::call(self, out);
}
// aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & square_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::square_out::call(self, out);
}

// aten::std(Tensor self, bool unbiased=True) -> Tensor
inline at::Tensor std(const at::Tensor & self, bool unbiased) {
    return at::_ops::std::call(self, unbiased);
}

// aten::std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor
inline at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
    return at::_ops::std_dim::call(self, dim, unbiased, keepdim);
}

// aten::std.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor
inline at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::std_correction::call(self, dim, correction, keepdim);
}

// aten::std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, bool unbiased) {
    return at::_ops::std_mean::call(self, unbiased);
}

// aten::std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
    return at::_ops::std_mean_dim::call(self, dim, unbiased, keepdim);
}

// aten::std_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::std_mean_correction::call(self, dim, correction, keepdim);
}

// aten::std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
    return at::_ops::std_mean_names_dim::call(self, dim, unbiased, keepdim);
}

// aten::std_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::std_mean_correction_names::call(self, dim, correction, keepdim);
}

// aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
    return at::_ops::std_out::call(self, dim, unbiased, keepdim, out);
}
// aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) {
    return at::_ops::std_out::call(self, dim, unbiased, keepdim, out);
}

// aten::std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::std_correction_out::call(self, dim, correction, keepdim, out);
}
// aten::std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out) {
    return at::_ops::std_correction_out::call(self, dim, correction, keepdim, out);
}

// aten::std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor
inline at::Tensor std(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
    return at::_ops::std_names_dim::call(self, dim, unbiased, keepdim);
}

// aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
    return at::_ops::std_names_out::call(self, dim, unbiased, keepdim, out);
}
// aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & std_outf(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) {
    return at::_ops::std_names_out::call(self, dim, unbiased, keepdim, out);
}

// aten::std.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor
inline at::Tensor std(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::std_correction_names::call(self, dim, correction, keepdim);
}

// aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::std_correction_names_out::call(self, dim, correction, keepdim, out);
}
// aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & std_outf(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out) {
    return at::_ops::std_correction_names_out::call(self, dim, correction, keepdim, out);
}

// aten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor prod(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::prod::call(self, dtype);
}

// aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor prod(const at::Tensor & self, int64_t dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::prod_dim_int::call(self, dim, keepdim, dtype);
}

// aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & prod_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::prod_int_out::call(self, dim, keepdim, dtype, out);
}
// aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & prod_outf(const at::Tensor & self, int64_t dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::prod_int_out::call(self, dim, keepdim, dtype, out);
}

// aten::prod.dim_Dimname(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor prod(const at::Tensor & self, at::Dimname dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::prod_dim_Dimname::call(self, dim, keepdim, dtype);
}

// aten::prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & prod_out(at::Tensor & out, const at::Tensor & self, at::Dimname dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::prod_Dimname_out::call(self, dim, keepdim, dtype, out);
}
// aten::prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & prod_outf(const at::Tensor & self, at::Dimname dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::prod_Dimname_out::call(self, dim, keepdim, dtype, out);
}

// aten::t(Tensor(a) self) -> Tensor(a)
inline at::Tensor t(const at::Tensor & self) {
    return at::_ops::t::call(self);
}

// aten::tan(Tensor self) -> Tensor
inline at::Tensor tan(const at::Tensor & self) {
    return at::_ops::tan::call(self);
}

// aten::tan_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & tan_(at::Tensor & self) {
    return at::_ops::tan_::call(self);
}

// aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tan_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::tan_out::call(self, out);
}
// aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tan_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::tan_out::call(self, out);
}

// aten::tanh(Tensor self) -> Tensor
inline at::Tensor tanh(const at::Tensor & self) {
    return at::_ops::tanh::call(self);
}

// aten::tanh_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & tanh_(at::Tensor & self) {
    return at::_ops::tanh_::call(self);
}

// aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tanh_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::tanh_out::call(self, out);
}
// aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tanh_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::tanh_out::call(self, out);
}

// aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor
inline at::Tensor tensordot(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) {
    return at::_ops::tensordot::call(self, other, dims_self, dims_other);
}

// aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tensordot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) {
    return at::_ops::tensordot_out::call(self, other, dims_self, dims_other, out);
}
// aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tensordot_outf(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out) {
    return at::_ops::tensordot_out::call(self, other, dims_self, dims_other, out);
}

// aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor
inline at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) {
    return at::_ops::threshold::call(self, threshold, value);
}

// aten::threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!)
inline at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) {
    return at::_ops::threshold_::call(self, threshold, value);
}

// aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & threshold_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) {
    return at::_ops::threshold_out::call(self, threshold, value, out);
}
// aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & threshold_outf(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::threshold_out::call(self, threshold, value, out);
}

// aten::threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & threshold_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) {
    return at::_ops::threshold_backward_grad_input::call(grad_output, self, threshold, grad_input);
}
// aten::threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & threshold_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input) {
    return at::_ops::threshold_backward_grad_input::call(grad_output, self, threshold, grad_input);
}

// aten::threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor
inline at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) {
    return at::_ops::threshold_backward::call(grad_output, self, threshold);
}

// aten::tile(Tensor self, SymInt[] dims) -> Tensor
inline at::Tensor tile(const at::Tensor & self, at::IntArrayRef dims) {
    return at::_ops::tile::call(self, c10::fromIntArrayRefSlow(dims));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor tile(const at::Tensor & self, at::IntArrayRef dims) {
    return at::_ops::tile::call(self, c10::fromIntArrayRefSlow(dims));
  }
}

// aten::tile(Tensor self, SymInt[] dims) -> Tensor
inline at::Tensor tile_symint(const at::Tensor & self, c10::SymIntArrayRef dims) {
    return at::_ops::tile::call(self, dims);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor tile(const at::Tensor & self, c10::SymIntArrayRef dims) {
    return at::_ops::tile::call(self, dims);
  }
}

// aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)
inline at::Tensor transpose(const at::Tensor & self, int64_t dim0, int64_t dim1) {
    return at::_ops::transpose_int::call(self, dim0, dim1);
}

// aten::transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a)
inline at::Tensor transpose(const at::Tensor & self, at::Dimname dim0, at::Dimname dim1) {
    return at::_ops::transpose_Dimname::call(self, dim0, dim1);
}

// aten::_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor
inline at::Tensor _mkldnn_transpose(const at::Tensor & self, int64_t dim0, int64_t dim1) {
    return at::_ops::_mkldnn_transpose::call(self, dim0, dim1);
}

// aten::_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)
inline at::Tensor & _mkldnn_transpose_(at::Tensor & self, int64_t dim0, int64_t dim1) {
    return at::_ops::_mkldnn_transpose_::call(self, dim0, dim1);
}

// aten::one_hot(Tensor self, int num_classes=-1) -> Tensor
inline at::Tensor one_hot(const at::Tensor & self, int64_t num_classes=-1) {
    return at::_ops::one_hot::call(self, num_classes);
}

// aten::flip(Tensor self, int[] dims) -> Tensor
inline at::Tensor flip(const at::Tensor & self, at::IntArrayRef dims) {
    return at::_ops::flip::call(self, dims);
}

// aten::fliplr(Tensor self) -> Tensor
inline at::Tensor fliplr(const at::Tensor & self) {
    return at::_ops::fliplr::call(self);
}

// aten::flipud(Tensor self) -> Tensor
inline at::Tensor flipud(const at::Tensor & self) {
    return at::_ops::flipud::call(self);
}

// aten::roll(Tensor self, SymInt[1] shifts, int[1] dims=[]) -> Tensor
inline at::Tensor roll(const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims={}) {
    return at::_ops::roll::call(self, c10::fromIntArrayRefSlow(shifts), dims);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor roll(const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims={}) {
    return at::_ops::roll::call(self, c10::fromIntArrayRefSlow(shifts), dims);
  }
}

// aten::roll(Tensor self, SymInt[1] shifts, int[1] dims=[]) -> Tensor
inline at::Tensor roll_symint(const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims={}) {
    return at::_ops::roll::call(self, shifts, dims);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor roll(const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims={}) {
    return at::_ops::roll::call(self, shifts, dims);
  }
}

// aten::rot90(Tensor self, int k=1, int[] dims=[0,1]) -> Tensor
inline at::Tensor rot90(const at::Tensor & self, int64_t k=1, at::IntArrayRef dims={0,1}) {
    return at::_ops::rot90::call(self, k, dims);
}

// aten::trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor
inline at::Tensor trapezoid(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) {
    return at::_ops::trapezoid_x::call(y, x, dim);
}

// aten::trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor
inline at::Tensor trapezoid(const at::Tensor & y, const at::Scalar & dx=1, int64_t dim=-1) {
    return at::_ops::trapezoid_dx::call(y, dx, dim);
}

// aten::trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor
inline at::Tensor trapz(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) {
    return at::_ops::trapz_x::call(y, x, dim);
}

// aten::trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor
inline at::Tensor trapz(const at::Tensor & y, double dx=1, int64_t dim=-1) {
    return at::_ops::trapz_dx::call(y, dx, dim);
}

// aten::_transform_bias_rescale_qkv(Tensor qkv, Tensor qkv_bias, int num_heads) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _transform_bias_rescale_qkv(const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads) {
    return at::_ops::_transform_bias_rescale_qkv::call(qkv, qkv_bias, num_heads);
}

// aten::_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor
inline at::Tensor _nested_tensor_from_mask(const at::Tensor & t, const at::Tensor & mask, bool mask_check=true) {
    return at::_ops::_nested_tensor_from_mask::call(t, mask, mask_check);
}

// aten::_nested_tensor_from_mask_left_aligned(Tensor t, Tensor mask) -> bool
inline bool _nested_tensor_from_mask_left_aligned(const at::Tensor & t, const at::Tensor & mask) {
    return at::_ops::_nested_tensor_from_mask_left_aligned::call(t, mask);
}

// aten::_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor
inline at::Tensor _nested_from_padded(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213=false) {
    return at::_ops::_nested_from_padded::call(padded, cpu_nested_shape_example, fuse_transform_0213);
}

// aten::_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor
inline at::Tensor _nested_from_padded_and_nested_example(const at::Tensor & padded, const at::Tensor & nt_example) {
    return at::_ops::_nested_from_padded_and_nested_example::call(padded, nt_example);
}

// aten::_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor(a)
inline at::Tensor _nested_view_from_buffer(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) {
    return at::_ops::_nested_view_from_buffer::call(self, nested_size, nested_strides, offsets);
}

// aten::_nested_view_from_buffer_copy(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor
inline at::Tensor _nested_view_from_buffer_copy(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) {
    return at::_ops::_nested_view_from_buffer_copy::call(self, nested_size, nested_strides, offsets);
}

// aten::_nested_view_from_jagged(Tensor(a) self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None) -> Tensor(a)
inline at::Tensor _nested_view_from_jagged(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional<at::Tensor> & lengths={}, int64_t ragged_idx=1, const ::std::optional<at::Tensor> & min_seqlen={}, const ::std::optional<at::Tensor> & max_seqlen={}) {
    return at::_ops::_nested_view_from_jagged::call(self, offsets, dummy, lengths, ragged_idx, min_seqlen, max_seqlen);
}

// aten::_nested_view_from_jagged_copy(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None) -> Tensor
inline at::Tensor _nested_view_from_jagged_copy(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional<at::Tensor> & lengths={}, int64_t ragged_idx=1, const ::std::optional<at::Tensor> & min_seqlen={}, const ::std::optional<at::Tensor> & max_seqlen={}) {
    return at::_ops::_nested_view_from_jagged_copy::call(self, offsets, dummy, lengths, ragged_idx, min_seqlen, max_seqlen);
}

// aten::_nested_get_values(Tensor(a) self) -> Tensor(a)
inline at::Tensor _nested_get_values(const at::Tensor & self) {
    return at::_ops::_nested_get_values::call(self);
}

// aten::_nested_get_values_copy(Tensor self) -> Tensor
inline at::Tensor _nested_get_values_copy(const at::Tensor & self) {
    return at::_ops::_nested_get_values_copy::call(self);
}

// aten::_nested_get_offsets(Tensor self) -> Tensor
inline at::Tensor _nested_get_offsets(const at::Tensor & self) {
    return at::_ops::_nested_get_offsets::call(self);
}

// aten::_nested_get_lengths(Tensor self) -> Tensor
inline at::Tensor _nested_get_lengths(const at::Tensor & self) {
    return at::_ops::_nested_get_lengths::call(self);
}

// aten::_nested_get_ragged_idx(Tensor self) -> int
inline int64_t _nested_get_ragged_idx(const at::Tensor & self) {
    return at::_ops::_nested_get_ragged_idx::call(self);
}

// aten::_nested_get_min_seqlen(Tensor self) -> Tensor
inline at::Tensor _nested_get_min_seqlen(const at::Tensor & self) {
    return at::_ops::_nested_get_min_seqlen::call(self);
}

// aten::_nested_get_max_seqlen(Tensor self) -> Tensor
inline at::Tensor _nested_get_max_seqlen(const at::Tensor & self) {
    return at::_ops::_nested_get_max_seqlen::call(self);
}

// aten::_nested_get_jagged_dummy(Tensor any) -> Tensor
inline at::Tensor _nested_get_jagged_dummy(const at::Tensor & any) {
    return at::_ops::_nested_get_jagged_dummy::call(any);
}

// aten::_nested_compute_contiguous_strides_offsets(Tensor nested_size) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _nested_compute_contiguous_strides_offsets(const at::Tensor & nested_size) {
    return at::_ops::_nested_compute_contiguous_strides_offsets::call(nested_size);
}

// aten::_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor
inline at::Tensor _trilinear(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim=1) {
    return at::_ops::_trilinear::call(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim);
}

// aten::triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, float margin=1.0, float p=2, float eps=1e-06, bool swap=False, int reduction=Mean) -> Tensor
inline at::Tensor triplet_margin_loss(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin=1.0, double p=2, double eps=1e-06, bool swap=false, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::triplet_margin_loss::call(anchor, positive, negative, margin, p, eps, swap, reduction);
}

// aten::trunc(Tensor self) -> Tensor
inline at::Tensor trunc(const at::Tensor & self) {
    return at::_ops::trunc::call(self);
}

// aten::trunc_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & trunc_(at::Tensor & self) {
    return at::_ops::trunc_::call(self);
}

// aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & trunc_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::trunc_out::call(self, out);
}
// aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & trunc_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::trunc_out::call(self, out);
}

// aten::fix(Tensor self) -> Tensor
inline at::Tensor fix(const at::Tensor & self) {
    return at::_ops::fix::call(self);
}

// aten::fix_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & fix_(at::Tensor & self) {
    return at::_ops::fix_::call(self);
}

// aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fix_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::fix_out::call(self, out);
}
// aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fix_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::fix_out::call(self, out);
}

// aten::_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool
inline bool _has_compatible_shallow_copy_type(const at::Tensor & self, const at::Tensor & from) {
    return at::_ops::_has_compatible_shallow_copy_type::call(self, from);
}

// aten::_unique(Tensor self, bool sorted=True, bool return_inverse=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _unique(const at::Tensor & self, bool sorted=true, bool return_inverse=false) {
    return at::_ops::_unique::call(self, sorted, return_inverse);
}

// aten::unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim(const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false) {
    return at::_ops::unique_dim::call(self, dim, sorted, return_inverse, return_counts);
}

// aten::unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive(const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::unique_consecutive::call(self, return_inverse, return_counts, dim);
}

// aten::unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_consecutive(const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false) {
    return at::_ops::unique_dim_consecutive::call(self, dim, return_inverse, return_counts);
}

// aten::_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _unique2(const at::Tensor & self, bool sorted=true, bool return_inverse=false, bool return_counts=false) {
    return at::_ops::_unique2::call(self, sorted, return_inverse, return_counts);
}

// aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor
inline at::Tensor _unsafe_view(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_unsafe_view::call(self, c10::fromIntArrayRefSlow(size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _unsafe_view(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_unsafe_view::call(self, c10::fromIntArrayRefSlow(size));
  }
}

// aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor
inline at::Tensor _unsafe_view_symint(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::_unsafe_view::call(self, size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _unsafe_view(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::_unsafe_view::call(self, size);
  }
}

// aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a)
inline at::Tensor unsqueeze(const at::Tensor & self, int64_t dim) {
    return at::_ops::unsqueeze::call(self, dim);
}

// aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor
inline at::Tensor vander(const at::Tensor & x, ::std::optional<int64_t> N=::std::nullopt, bool increasing=false) {
    return at::_ops::vander::call(x, N, increasing);
}

// aten::var(Tensor self, bool unbiased=True) -> Tensor
inline at::Tensor var(const at::Tensor & self, bool unbiased) {
    return at::_ops::var::call(self, unbiased);
}

// aten::var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor
inline at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
    return at::_ops::var_dim::call(self, dim, unbiased, keepdim);
}

// aten::var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor
inline at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::var_correction::call(self, dim, correction, keepdim);
}

// aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
    return at::_ops::var_out::call(self, dim, unbiased, keepdim, out);
}
// aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & var_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) {
    return at::_ops::var_out::call(self, dim, unbiased, keepdim, out);
}

// aten::var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::var_correction_out::call(self, dim, correction, keepdim, out);
}
// aten::var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & var_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out) {
    return at::_ops::var_correction_out::call(self, dim, correction, keepdim, out);
}

// aten::var.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor
inline at::Tensor var(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
    return at::_ops::var_names_dim::call(self, dim, unbiased, keepdim);
}

// aten::var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
    return at::_ops::var_names_out::call(self, dim, unbiased, keepdim, out);
}
// aten::var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & var_outf(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) {
    return at::_ops::var_names_out::call(self, dim, unbiased, keepdim, out);
}

// aten::var.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor
inline at::Tensor var(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::var_correction_names::call(self, dim, correction, keepdim);
}

// aten::var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::var_correction_names_out::call(self, dim, correction, keepdim, out);
}
// aten::var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & var_outf(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out) {
    return at::_ops::var_correction_names_out::call(self, dim, correction, keepdim, out);
}

// aten::var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, bool unbiased) {
    return at::_ops::var_mean::call(self, unbiased);
}

// aten::var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
    return at::_ops::var_mean_dim::call(self, dim, unbiased, keepdim);
}

// aten::var_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::var_mean_correction::call(self, dim, correction, keepdim);
}

// aten::var_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
    return at::_ops::var_mean_names_dim::call(self, dim, unbiased, keepdim);
}

// aten::var_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::var_mean_correction_names::call(self, dim, correction, keepdim);
}

// aten::where.self(Tensor condition, Tensor self, Tensor other) -> Tensor
inline at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::where_self::call(condition, self, other);
}

// aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & where_out(at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::where_self_out::call(condition, self, other, out);
}
// aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & where_outf(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::where_self_out::call(condition, self, other, out);
}

// aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor
inline at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::where_ScalarSelf::call(condition, self, other);
}

// aten::where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor
inline at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::where_ScalarOther::call(condition, self, other);
}

// aten::where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor
inline at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other) {
    return at::_ops::where_Scalar::call(condition, self, other);
}

// aten::where(Tensor condition) -> Tensor[]
inline ::std::vector<at::Tensor> where(const at::Tensor & condition) {
    return at::_ops::where::call(condition);
}

// aten::norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor
inline at::Tensor norm_except_dim(const at::Tensor & v, int64_t pow=2, int64_t dim=0) {
    return at::_ops::norm_except_dim::call(v, pow, dim);
}

// aten::_weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor
inline at::Tensor _weight_norm(const at::Tensor & v, const at::Tensor & g, int64_t dim=0) {
    return at::_ops::_weight_norm::call(v, g, dim);
}

// aten::_weight_norm_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _weight_norm_interface(const at::Tensor & v, const at::Tensor & g, int64_t dim=0) {
    return at::_ops::_weight_norm_interface::call(v, g, dim);
}

// aten::_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _weight_norm_interface_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) {
    return at::_ops::_weight_norm_interface_backward::call(grad_w, saved_v, saved_g, saved_norms, dim);
}

// aten::_weight_norm_differentiable_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _weight_norm_differentiable_backward(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) {
    return at::_ops::_weight_norm_differentiable_backward::call(grad_w, saved_v, saved_g, saved_norms, dim);
}

// aten::zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
    return at::_ops::zeros_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::zeros_names::call(size, names, dtype, layout, device, pin_memory);
}

// aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _efficientzerotensor(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_efficientzerotensor::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _efficientzerotensor(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_efficientzerotensor::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _efficientzerotensor(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_efficientzerotensor::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _efficientzerotensor(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_efficientzerotensor::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _efficientzerotensor_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_efficientzerotensor::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _efficientzerotensor(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_efficientzerotensor::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _efficientzerotensor_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_efficientzerotensor::call(size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _efficientzerotensor(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_efficientzerotensor::call(size, dtype, layout, device, pin_memory);
  }
}

// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor zeros_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::zeros::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor zeros(c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::zeros::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor zeros_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::zeros::call(size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor zeros(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::zeros::call(size, dtype, layout, device, pin_memory);
  }
}

// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zeros_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & zeros_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zeros_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::zeros_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & zeros_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::zeros_out::call(size, out);
  }
}

// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zeros_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::zeros_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & zeros_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::zeros_out::call(size, out);
  }
}

// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor zeros_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::zeros_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::zeros_like::call(self, dtype, layout, device, pin_memory, memory_format);
}

// aten::_standard_gamma_grad(Tensor self, Tensor output) -> Tensor
inline at::Tensor _standard_gamma_grad(const at::Tensor & self, const at::Tensor & output) {
    return at::_ops::_standard_gamma_grad::call(self, output);
}

// aten::_standard_gamma(Tensor self, Generator? generator=None) -> Tensor
inline at::Tensor _standard_gamma(const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::_standard_gamma::call(self, generator);
}

// aten::_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor
inline at::Tensor _dirichlet_grad(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) {
    return at::_ops::_dirichlet_grad::call(x, alpha, total);
}

// aten::_sample_dirichlet(Tensor self, Generator? generator=None) -> Tensor
inline at::Tensor _sample_dirichlet(const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::_sample_dirichlet::call(self, generator);
}

// aten::poisson(Tensor self, Generator? generator=None) -> Tensor
inline at::Tensor poisson(const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::poisson::call(self, generator);
}

// aten::binomial(Tensor count, Tensor prob, Generator? generator=None) -> Tensor
inline at::Tensor binomial(const at::Tensor & count, const at::Tensor & prob, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::binomial::call(count, prob, generator);
}

// aten::native_norm(Tensor self, Scalar p=2) -> Tensor
inline at::Tensor native_norm(const at::Tensor & self, const at::Scalar & p=2) {
    return at::_ops::native_norm::call(self, p);
}

// aten::native_norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype) -> Tensor
inline at::Tensor native_norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype) {
    return at::_ops::native_norm_ScalarOpt_dim_dtype::call(self, p, dim, keepdim, dtype);
}

// aten::_batch_norm_with_update(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _batch_norm_with_update(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, double momentum, double eps) {
    return at::_ops::_batch_norm_with_update::call(input, weight, bias, running_mean, running_var, momentum, eps);
}

// aten::_batch_norm_with_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd, Tensor(g!) reserve) -> (Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _batch_norm_with_update_out(at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, at::Tensor & reserve, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, double momentum, double eps) {
    return at::_ops::_batch_norm_with_update_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out, save_mean, save_invstd, reserve);
}
// aten::_batch_norm_with_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd, Tensor(g!) reserve) -> (Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _batch_norm_with_update_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, at::Tensor & reserve) {
    return at::_ops::_batch_norm_with_update_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out, save_mean, save_invstd, reserve);
}

// aten::_batch_norm_no_update(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _batch_norm_no_update(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps) {
    return at::_ops::_batch_norm_no_update::call(input, weight, bias, running_mean, running_var, momentum, eps);
}

// aten::batch_norm_backward(Tensor grad_out, Tensor input, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, bool update, float eps, bool[3] output_mask, Tensor reserve) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, bool update, double eps, ::std::array<bool,3> output_mask, const at::Tensor & reserve) {
    return at::_ops::batch_norm_backward::call(grad_out, input, weight, running_mean, running_var, save_mean, save_var, update, eps, output_mask, reserve);
}

// aten::_sparse_sum(Tensor self) -> Tensor
inline at::Tensor _sparse_sum(const at::Tensor & self) {
    return at::_ops::_sparse_sum::call(self);
}

// aten::_sparse_sum.dtype(Tensor self, *, ScalarType dtype) -> Tensor
inline at::Tensor _sparse_sum(const at::Tensor & self, at::ScalarType dtype) {
    return at::_ops::_sparse_sum_dtype::call(self, dtype);
}

// aten::_sparse_sum.dim(Tensor self, int[1] dim) -> Tensor
inline at::Tensor _sparse_sum(const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::_sparse_sum_dim::call(self, dim);
}

// aten::_sparse_sum.dim_dtype(Tensor self, int[1] dim, *, ScalarType dtype) -> Tensor
inline at::Tensor _sparse_sum(const at::Tensor & self, at::IntArrayRef dim, at::ScalarType dtype) {
    return at::_ops::_sparse_sum_dim_dtype::call(self, dim, dtype);
}

// aten::_sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor
inline at::Tensor _sparse_sum_backward(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::_sparse_sum_backward::call(grad, self, dim);
}

// aten::_sparse_csr_sum.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor _sparse_csr_sum(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_sparse_csr_sum_dim_dtype::call(self, dim, keepdim, dtype);
}

// aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor _sparse_csr_prod(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_sparse_csr_prod_dim_dtype::call(self, dim, keepdim, dtype);
}

// aten::_sparse_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
inline at::Tensor _sparse_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_sparse_softmax_int::call(self, dim, dtype);
}

// aten::_sparse_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor _sparse_softmax(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_sparse_softmax_Dimname::call(self, dim, dtype);
}

// aten::_sparse_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
inline at::Tensor _sparse_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) {
    return at::_ops::_sparse_softmax::call(self, dim, half_to_float);
}

// aten::_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor
inline at::Tensor _sparse_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) {
    return at::_ops::_sparse_softmax_backward_data::call(grad_output, output, dim, self);
}

// aten::_sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
inline at::Tensor _sparse_log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_sparse_log_softmax_int::call(self, dim, dtype);
}

// aten::_sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor _sparse_log_softmax(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_sparse_log_softmax_Dimname::call(self, dim, dtype);
}

// aten::_sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
inline at::Tensor _sparse_log_softmax(const at::Tensor & self, int64_t dim, bool half_to_float) {
    return at::_ops::_sparse_log_softmax::call(self, dim, half_to_float);
}

// aten::_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor
inline at::Tensor _sparse_log_softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) {
    return at::_ops::_sparse_log_softmax_backward_data::call(grad_output, output, dim, self);
}

// aten::_spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor
inline at::Tensor _spdiags(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, ::std::optional<at::Layout> layout=::std::nullopt) {
    return at::_ops::_spdiags::call(diagonals, offsets, shape, layout);
}

// aten::norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor
inline at::Tensor norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::ScalarType dtype) {
    return at::_ops::norm_ScalarOpt_dtype::call(self, p, dtype);
}

// aten::norm.Scalar(Tensor self, Scalar p=2) -> Tensor
inline at::Tensor norm(const at::Tensor & self, const at::Scalar & p=2) {
    return at::_ops::norm_Scalar::call(self, p);
}

// aten::norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor
inline at::Tensor norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) {
    return at::_ops::norm_ScalarOpt_dim_dtype::call(self, p, dim, keepdim, dtype);
}

// aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> Tensor
inline at::Tensor norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::norm_ScalarOpt_dim::call(self, p, dim, keepdim);
}

// aten::norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) {
    return at::_ops::norm_dtype_out::call(self, p, dim, keepdim, dtype, out);
}
// aten::norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype, at::Tensor & out) {
    return at::_ops::norm_dtype_out::call(self, p, dim, keepdim, dtype, out);
}

// aten::norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::norm_out::call(self, p, dim, keepdim, out);
}
// aten::norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::norm_out::call(self, p, dim, keepdim, out);
}

// aten::norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor
inline at::Tensor norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype) {
    return at::_ops::norm_names_ScalarOpt_dim_dtype::call(self, p, dim, keepdim, dtype);
}

// aten::norm.names_ScalarOpt_dim(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False) -> Tensor
inline at::Tensor norm(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim=false) {
    return at::_ops::norm_names_ScalarOpt_dim::call(self, p, dim, keepdim);
}

// aten::norm.names_dtype_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype) {
    return at::_ops::norm_names_dtype_out::call(self, p, dim, keepdim, dtype, out);
}
// aten::norm.names_dtype_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype, at::Tensor & out) {
    return at::_ops::norm_names_dtype_out::call(self, p, dim, keepdim, dtype, out);
}

// aten::norm.names_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim=false) {
    return at::_ops::norm_names_out::call(self, p, dim, keepdim, out);
}
// aten::norm.names_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::DimnameList dim, bool keepdim, at::Tensor & out) {
    return at::_ops::norm_names_out::call(self, p, dim, keepdim, out);
}

// aten::frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent)
inline ::std::tuple<at::Tensor,at::Tensor> frexp(const at::Tensor & self) {
    return at::_ops::frexp_Tensor::call(self);
}

// aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent)
inline ::std::tuple<at::Tensor &,at::Tensor &> frexp_out(at::Tensor & mantissa, at::Tensor & exponent, const at::Tensor & self) {
    return at::_ops::frexp_Tensor_out::call(self, mantissa, exponent);
}
// aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent)
inline ::std::tuple<at::Tensor &,at::Tensor &> frexp_outf(const at::Tensor & self, at::Tensor & mantissa, at::Tensor & exponent) {
    return at::_ops::frexp_Tensor_out::call(self, mantissa, exponent);
}

// aten::frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor
inline at::Tensor frobenius_norm(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::frobenius_norm_dim::call(self, dim, keepdim);
}

// aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & frobenius_norm_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::frobenius_norm_out::call(self, dim, keepdim, out);
}
// aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & frobenius_norm_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::frobenius_norm_out::call(self, dim, keepdim, out);
}

// aten::nuclear_norm(Tensor self, bool keepdim=False) -> Tensor
inline at::Tensor nuclear_norm(const at::Tensor & self, bool keepdim=false) {
    return at::_ops::nuclear_norm::call(self, keepdim);
}

// aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nuclear_norm_out(at::Tensor & out, const at::Tensor & self, bool keepdim=false) {
    return at::_ops::nuclear_norm_out::call(self, keepdim, out);
}
// aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nuclear_norm_outf(const at::Tensor & self, bool keepdim, at::Tensor & out) {
    return at::_ops::nuclear_norm_out::call(self, keepdim, out);
}

// aten::nuclear_norm.dim(Tensor self, int[2] dim, bool keepdim=False) -> Tensor
inline at::Tensor nuclear_norm(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::nuclear_norm_dim::call(self, dim, keepdim);
}

// aten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nuclear_norm_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::nuclear_norm_dim_out::call(self, dim, keepdim, out);
}
// aten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nuclear_norm_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::nuclear_norm_dim_out::call(self, dim, keepdim, out);
}

// aten::clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor clone(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::clone::call(self, memory_format);
}

// aten::positive(Tensor(a) self) -> Tensor(a)
inline at::Tensor positive(const at::Tensor & self) {
    return at::_ops::positive::call(self);
}

// aten::resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!)
inline const at::Tensor & resize_as_(const at::Tensor & self, const at::Tensor & the_template, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_as_::call(self, the_template, memory_format);
}

// aten::resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!)
inline const at::Tensor & resize_as_sparse_(const at::Tensor & self, const at::Tensor & the_template) {
    return at::_ops::resize_as_sparse_::call(self, the_template);
}

// aten::zero_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & zero_(at::Tensor & self) {
    return at::_ops::zero_::call(self);
}

// aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::sub_out::call(self, other, alpha, out);
}
// aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sub_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::sub_out::call(self, other, alpha, out);
}

// aten::sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
inline at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::sub_Tensor::call(self, other, alpha);
}

// aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
inline at::Tensor sub(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::sub_Scalar::call(self, other, alpha);
}

// aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & subtract_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::subtract_out::call(self, other, alpha, out);
}
// aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & subtract_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::subtract_out::call(self, other, alpha, out);
}

// aten::subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
inline at::Tensor subtract(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::subtract_Tensor::call(self, other, alpha);
}

// aten::subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
inline at::Tensor subtract(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::subtract_Scalar::call(self, other, alpha);
}

// aten::rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
inline at::Tensor rsub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::rsub_Tensor::call(self, other, alpha);
}

// aten::heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & heaviside_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & values) {
    return at::_ops::heaviside_out::call(self, values, out);
}
// aten::heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & heaviside_outf(const at::Tensor & self, const at::Tensor & values, at::Tensor & out) {
    return at::_ops::heaviside_out::call(self, values, out);
}

// aten::heaviside(Tensor self, Tensor values) -> Tensor
inline at::Tensor heaviside(const at::Tensor & self, const at::Tensor & values) {
    return at::_ops::heaviside::call(self, values);
}

// aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
inline at::Tensor rsub(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::rsub_Scalar::call(self, other, alpha);
}

// aten::_sparse_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inline at::Tensor _sparse_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::_sparse_addmm::call(self, mat1, mat2, beta, alpha);
}

// aten::sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sparse_sampled_addmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::sparse_sampled_addmm_out::call(self, mat1, mat2, beta, alpha, out);
}
// aten::sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sparse_sampled_addmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::sparse_sampled_addmm_out::call(self, mat1, mat2, beta, alpha, out);
}

// aten::sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inline at::Tensor sparse_sampled_addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::sparse_sampled_addmm::call(self, mat1, mat2, beta, alpha);
}

// aten::_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _sparse_mm_reduce_impl(const at::Tensor & self, const at::Tensor & other, c10::string_view reduce) {
    return at::_ops::_sparse_mm_reduce_impl::call(self, other, reduce);
}

// aten::_sparse_mm_reduce_impl_backward(Tensor self, Tensor grad_out, Tensor weight, str reduce, Tensor arg_out, bool[2] output_mask) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _sparse_mm_reduce_impl_backward(const at::Tensor & self, const at::Tensor & grad_out, const at::Tensor & weight, c10::string_view reduce, const at::Tensor & arg_out, ::std::array<bool,2> output_mask) {
    return at::_ops::_sparse_mm_reduce_impl_backward::call(self, grad_out, weight, reduce, arg_out, output_mask);
}

// aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addmm_out::call(self, mat1, mat2, beta, alpha, out);
}
// aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::addmm_out::call(self, mat1, mat2, beta, alpha, out);
}

// aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inline at::Tensor addmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addmm::call(self, mat1, mat2, beta, alpha);
}

// aten::_addmm_activation.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _addmm_activation_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1, bool use_gelu=false) {
    return at::_ops::_addmm_activation_out::call(self, mat1, mat2, beta, alpha, use_gelu, out);
}
// aten::_addmm_activation.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _addmm_activation_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu, at::Tensor & out) {
    return at::_ops::_addmm_activation_out::call(self, mat1, mat2, beta, alpha, use_gelu, out);
}

// aten::_addmm_activation(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False) -> Tensor
inline at::Tensor _addmm_activation(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1, bool use_gelu=false) {
    return at::_ops::_addmm_activation::call(self, mat1, mat2, beta, alpha, use_gelu);
}

// aten::_scaled_mm(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False) -> Tensor
inline at::Tensor _scaled_mm(const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional<at::Tensor> & bias={}, const ::std::optional<at::Tensor> & scale_result={}, ::std::optional<at::ScalarType> out_dtype=::std::nullopt, bool use_fast_accum=false) {
    return at::_ops::_scaled_mm::call(self, mat2, scale_a, scale_b, bias, scale_result, out_dtype, use_fast_accum);
}

// aten::_scaled_mm.out(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _scaled_mm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional<at::Tensor> & bias={}, const ::std::optional<at::Tensor> & scale_result={}, ::std::optional<at::ScalarType> out_dtype=::std::nullopt, bool use_fast_accum=false) {
    return at::_ops::_scaled_mm_out::call(self, mat2, scale_a, scale_b, bias, scale_result, out_dtype, use_fast_accum, out);
}
// aten::_scaled_mm.out(Tensor self, Tensor mat2, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None, bool use_fast_accum=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _scaled_mm_outf(const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scale_a, const at::Tensor & scale_b, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & scale_result, ::std::optional<at::ScalarType> out_dtype, bool use_fast_accum, at::Tensor & out) {
    return at::_ops::_scaled_mm_out::call(self, mat2, scale_a, scale_b, bias, scale_result, out_dtype, use_fast_accum, out);
}

// aten::_sparse_compressed_tensor_with_dims(int nnz, int dense_dim, int[] size, int[] blocksize, ScalarType index_dtype, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor _sparse_compressed_tensor_with_dims(int64_t nnz, int64_t dense_dim, at::IntArrayRef size, at::IntArrayRef blocksize, at::ScalarType index_dtype, at::TensorOptions options) {
    return at::_ops::_sparse_compressed_tensor_with_dims::call(nnz, dense_dim, size, blocksize, index_dtype, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::_sparse_compressed_tensor_with_dims(int nnz, int dense_dim, int[] size, int[] blocksize, ScalarType index_dtype, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor _sparse_compressed_tensor_with_dims(int64_t nnz, int64_t dense_dim, at::IntArrayRef size, at::IntArrayRef blocksize, at::ScalarType index_dtype, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_compressed_tensor_with_dims::call(nnz, dense_dim, size, blocksize, index_dtype, dtype, layout, device, pin_memory);
}

// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_compressed_tensor_symint(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_compressed_tensor_symint(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory);
  }
}

// aten::sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_csr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_csr_tensor_crow_col_value_size::call(crow_indices, col_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_csr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_csr_tensor_crow_col_value_size::call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory);
}

// aten::sparse_csc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_csc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_csc_tensor_ccol_row_value_size::call(ccol_indices, row_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_csc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_csc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_csc_tensor_ccol_row_value_size::call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory);
}

// aten::sparse_bsr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_bsr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_bsr_tensor_crow_col_value_size::call(crow_indices, col_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_bsr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_bsr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_bsr_tensor_crow_col_value_size::call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory);
}

// aten::sparse_bsc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_bsc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_bsc_tensor_ccol_row_value_size::call(ccol_indices, row_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_bsc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_bsc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_bsc_tensor_ccol_row_value_size::call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory);
}

// aten::sparse_compressed_tensor.comp_plain_value(Tensor compressed_indices, Tensor plain_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::TensorOptions options) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value::call(compressed_indices, plain_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_compressed_tensor.comp_plain_value(Tensor compressed_indices, Tensor plain_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_compressed_tensor_comp_plain_value::call(compressed_indices, plain_indices, values, dtype, layout, device, pin_memory);
}

// aten::sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_csr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::TensorOptions options) {
    return at::_ops::sparse_csr_tensor_crow_col_value::call(crow_indices, col_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_csr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_csr_tensor_crow_col_value::call(crow_indices, col_indices, values, dtype, layout, device, pin_memory);
}

// aten::sparse_csc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_csc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::TensorOptions options) {
    return at::_ops::sparse_csc_tensor_ccol_row_value::call(ccol_indices, row_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_csc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_csc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_csc_tensor_ccol_row_value::call(ccol_indices, row_indices, values, dtype, layout, device, pin_memory);
}

// aten::sparse_bsr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_bsr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::TensorOptions options) {
    return at::_ops::sparse_bsr_tensor_crow_col_value::call(crow_indices, col_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_bsr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_bsr_tensor(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_bsr_tensor_crow_col_value::call(crow_indices, col_indices, values, dtype, layout, device, pin_memory);
}

// aten::sparse_bsc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_bsc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::TensorOptions options) {
    return at::_ops::sparse_bsc_tensor_ccol_row_value::call(ccol_indices, row_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_bsc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_bsc_tensor(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_bsc_tensor_ccol_row_value::call(ccol_indices, row_indices, values, dtype, layout, device, pin_memory);
}

// aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
  }
}

// aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_compressed_tensor_unsafe_symint(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_compressed_tensor_unsafe_symint(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _sparse_compressed_tensor_unsafe(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory);
  }
}

// aten::_sparse_csr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_csr_tensor_unsafe(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_sparse_csr_tensor_unsafe::call(crow_indices, col_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::_sparse_csr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_csr_tensor_unsafe(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_csr_tensor_unsafe::call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory);
}

// aten::_sparse_csc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_csc_tensor_unsafe(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_sparse_csc_tensor_unsafe::call(ccol_indices, row_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::_sparse_csc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_csc_tensor_unsafe(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_csc_tensor_unsafe::call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory);
}

// aten::_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_bsr_tensor_unsafe(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_sparse_bsr_tensor_unsafe::call(crow_indices, col_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_bsr_tensor_unsafe(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_bsr_tensor_unsafe::call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory);
}

// aten::_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_bsc_tensor_unsafe(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) {
    return at::_ops::_sparse_bsc_tensor_unsafe::call(ccol_indices, row_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _sparse_bsc_tensor_unsafe(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_bsc_tensor_unsafe::call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory);
}

// aten::sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_coo_tensor(at::IntArrayRef size, at::TensorOptions options) {
    return at::_ops::sparse_coo_tensor_size::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor sparse_coo_tensor(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::sparse_coo_tensor_size::call(size, dtype, layout, device, pin_memory);
}

// aten::sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
inline at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options={}, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::sparse_coo_tensor_indices::call(indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
}
// aten::sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
inline at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::sparse_coo_tensor_indices::call(indices, values, dtype, layout, device, pin_memory, is_coalesced);
}

// aten::sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
inline at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::sparse_coo_tensor_indices_size::call(indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
}
// aten::sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
inline at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::sparse_coo_tensor_indices_size::call(indices, values, size, dtype, layout, device, pin_memory, is_coalesced);
}

// aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
inline at::Tensor _sparse_coo_tensor_unsafe(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _sparse_coo_tensor_unsafe(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
  }
}

// aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
inline at::Tensor _sparse_coo_tensor_unsafe(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, is_coalesced);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _sparse_coo_tensor_unsafe(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, is_coalesced);
  }
}

// aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
inline at::Tensor _sparse_coo_tensor_unsafe_symint(const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options={}, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _sparse_coo_tensor_unsafe(const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options={}, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
  }
}

// aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
inline at::Tensor _sparse_coo_tensor_unsafe_symint(const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, size, dtype, layout, device, pin_memory, is_coalesced);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _sparse_coo_tensor_unsafe(const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, size, dtype, layout, device, pin_memory, is_coalesced);
  }
}

// aten::_validate_sparse_coo_tensor_args(Tensor indices, Tensor values, int[] size, bool? is_coalesced=None) -> ()
inline void _validate_sparse_coo_tensor_args(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_validate_sparse_coo_tensor_args::call(indices, values, size, is_coalesced);
}

// aten::_validate_sparse_compressed_tensor_args(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, Layout layout) -> ()
inline void _validate_sparse_compressed_tensor_args(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::Layout layout) {
    return at::_ops::_validate_sparse_compressed_tensor_args::call(compressed_indices, plain_indices, values, size, layout);
}

// aten::_validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> ()
inline void _validate_sparse_csr_tensor_args(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) {
    return at::_ops::_validate_sparse_csr_tensor_args::call(crow_indices, col_indices, values, size);
}

// aten::_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> ()
inline void _validate_sparse_csc_tensor_args(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) {
    return at::_ops::_validate_sparse_csc_tensor_args::call(ccol_indices, row_indices, values, size);
}

// aten::_validate_sparse_bsr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> ()
inline void _validate_sparse_bsr_tensor_args(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) {
    return at::_ops::_validate_sparse_bsr_tensor_args::call(crow_indices, col_indices, values, size);
}

// aten::_validate_sparse_bsc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> ()
inline void _validate_sparse_bsc_tensor_args(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) {
    return at::_ops::_validate_sparse_bsc_tensor_args::call(ccol_indices, row_indices, values, size);
}

// aten::_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor _sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, at::TensorOptions options) {
    return at::_ops::_sparse_coo_tensor_with_dims::call(sparse_dim, dense_dim, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
inline at::Tensor _sparse_coo_tensor_with_dims(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::_sparse_coo_tensor_with_dims::call(sparse_dim, dense_dim, size, dtype, layout, device, pin_memory);
}

// aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor
inline at::Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
  }
}

// aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor
inline at::Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, dtype, layout, device, pin_memory, is_coalesced);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, dtype, layout, device, pin_memory, is_coalesced);
  }
}

// aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor
inline at::Tensor _sparse_coo_tensor_with_dims_and_tensors_symint(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, size, indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, size, indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
  }
}

// aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor
inline at::Tensor _sparse_coo_tensor_with_dims_and_tensors_symint(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, size, indices, values, dtype, layout, device, pin_memory, is_coalesced);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _sparse_coo_tensor_with_dims_and_tensors(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<bool> is_coalesced) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, size, indices, values, dtype, layout, device, pin_memory, is_coalesced);
  }
}

// aten::_to_cpu(Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> _to_cpu(at::TensorList tensors) {
    return at::_ops::_to_cpu::call(tensors);
}

// aten::to_dense_backward(Tensor grad, Tensor input, bool? masked_grad=None) -> Tensor
inline at::Tensor to_dense_backward(const at::Tensor & grad, const at::Tensor & input, ::std::optional<bool> masked_grad=::std::nullopt) {
    return at::_ops::to_dense_backward::call(grad, input, masked_grad);
}

// aten::_coalesce(Tensor self) -> Tensor
inline at::Tensor _coalesce(const at::Tensor & self) {
    return at::_ops::_coalesce::call(self);
}

// aten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hspmm_out(at::Tensor & out, const at::Tensor & mat1, const at::Tensor & mat2) {
    return at::_ops::hspmm_out::call(mat1, mat2, out);
}
// aten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hspmm_outf(const at::Tensor & mat1, const at::Tensor & mat2, at::Tensor & out) {
    return at::_ops::hspmm_out::call(mat1, mat2, out);
}

// aten::hspmm(Tensor mat1, Tensor mat2) -> Tensor
inline at::Tensor hspmm(const at::Tensor & mat1, const at::Tensor & mat2) {
    return at::_ops::hspmm::call(mat1, mat2);
}

// aten::copy_sparse_to_sparse_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!)
inline at::Tensor & copy_sparse_to_sparse_(at::Tensor & self, const at::Tensor & src, bool non_blocking=false) {
    return at::_ops::copy_sparse_to_sparse_::call(self, src, non_blocking);
}

// aten::unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[]
inline ::std::vector<at::Tensor> unbind(const at::Tensor & self, int64_t dim=0) {
    return at::_ops::unbind_int::call(self, dim);
}

// aten::unbind.Dimname(Tensor(a -> *) self, Dimname dim) -> Tensor(a)[]
inline ::std::vector<at::Tensor> unbind(const at::Tensor & self, at::Dimname dim) {
    return at::_ops::unbind_Dimname::call(self, dim);
}

// aten::_to_sparse_semi_structured(Tensor dense) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _to_sparse_semi_structured(const at::Tensor & dense) {
    return at::_ops::_to_sparse_semi_structured::call(dense);
}

// aten::mkldnn_reorder_conv2d_weight(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor
inline at::Tensor mkldnn_reorder_conv2d_weight(const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv2d_weight::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor mkldnn_reorder_conv2d_weight(const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv2d_weight::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt);
  }
}

// aten::mkldnn_reorder_conv2d_weight(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor
inline at::Tensor mkldnn_reorder_conv2d_weight_symint(const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv2d_weight::call(self, padding, stride, dilation, groups, input_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor mkldnn_reorder_conv2d_weight(const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv2d_weight::call(self, padding, stride, dilation, groups, input_size);
  }
}

// aten::mkldnn_reorder_conv3d_weight(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor
inline at::Tensor mkldnn_reorder_conv3d_weight(const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv3d_weight::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor mkldnn_reorder_conv3d_weight(const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv3d_weight::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt);
  }
}

// aten::mkldnn_reorder_conv3d_weight(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor
inline at::Tensor mkldnn_reorder_conv3d_weight_symint(const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv3d_weight::call(self, padding, stride, dilation, groups, input_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor mkldnn_reorder_conv3d_weight(const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv3d_weight::call(self, padding, stride, dilation, groups, input_size);
  }
}

// aten::to_mkldnn_backward(Tensor grad, Tensor input) -> Tensor
inline at::Tensor to_mkldnn_backward(const at::Tensor & grad, const at::Tensor & input) {
    return at::_ops::to_mkldnn_backward::call(grad, input);
}

// aten::quantize_per_tensor_dynamic(Tensor self, ScalarType dtype, bool reduce_range) -> Tensor
inline at::Tensor quantize_per_tensor_dynamic(const at::Tensor & self, at::ScalarType dtype, bool reduce_range) {
    return at::_ops::quantize_per_tensor_dynamic::call(self, dtype, reduce_range);
}

// aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor
inline at::Tensor quantize_per_tensor(const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype) {
    return at::_ops::quantize_per_tensor::call(self, scale, zero_point, dtype);
}

// aten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor
inline at::Tensor quantize_per_tensor(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype) {
    return at::_ops::quantize_per_tensor_tensor_qparams::call(self, scale, zero_point, dtype);
}

// aten::quantize_per_tensor.tensors(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype) -> Tensor[]
inline ::std::vector<at::Tensor> quantize_per_tensor(at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype) {
    return at::_ops::quantize_per_tensor_tensors::call(tensors, scales, zero_points, dtype);
}

// aten::quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor
inline at::Tensor quantize_per_channel(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) {
    return at::_ops::quantize_per_channel::call(self, scales, zero_points, axis, dtype);
}

// aten::dequantize.self(Tensor self) -> Tensor
inline at::Tensor dequantize(const at::Tensor & self) {
    return at::_ops::dequantize_self::call(self);
}

// aten::dequantize.tensors(Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> dequantize(at::TensorList tensors) {
    return at::_ops::dequantize_tensors::call(tensors);
}

// aten::q_scale(Tensor self) -> float
inline double q_scale(const at::Tensor & self) {
    return at::_ops::q_scale::call(self);
}

// aten::q_zero_point(Tensor self) -> int
inline int64_t q_zero_point(const at::Tensor & self) {
    return at::_ops::q_zero_point::call(self);
}

// aten::q_per_channel_scales(Tensor self) -> Tensor
inline at::Tensor q_per_channel_scales(const at::Tensor & self) {
    return at::_ops::q_per_channel_scales::call(self);
}

// aten::q_per_channel_zero_points(Tensor self) -> Tensor
inline at::Tensor q_per_channel_zero_points(const at::Tensor & self) {
    return at::_ops::q_per_channel_zero_points::call(self);
}

// aten::q_per_channel_axis(Tensor self) -> int
inline int64_t q_per_channel_axis(const at::Tensor & self) {
    return at::_ops::q_per_channel_axis::call(self);
}

// aten::int_repr(Tensor self) -> Tensor
inline at::Tensor int_repr(const at::Tensor & self) {
    return at::_ops::int_repr::call(self);
}

// aten::_make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor
inline at::Tensor _make_per_tensor_quantized_tensor(const at::Tensor & self, double scale, int64_t zero_point) {
    return at::_ops::_make_per_tensor_quantized_tensor::call(self, scale, zero_point);
}

// aten::_make_per_channel_quantized_tensor(Tensor self, Tensor scale, Tensor zero_point, int axis) -> Tensor
inline at::Tensor _make_per_channel_quantized_tensor(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis) {
    return at::_ops::_make_per_channel_quantized_tensor::call(self, scale, zero_point, axis);
}

// aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor
inline at::Tensor fake_quantize_per_tensor_affine(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) {
    return at::_ops::fake_quantize_per_tensor_affine::call(self, scale, zero_point, quant_min, quant_max);
}

// aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor
inline at::Tensor fake_quantize_per_tensor_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max) {
    return at::_ops::fake_quantize_per_tensor_affine_tensor_qparams::call(self, scale, zero_point, quant_min, quant_max);
}

// aten::fake_quantize_per_tensor_affine_cachemask(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor output, Tensor mask)
inline ::std::tuple<at::Tensor,at::Tensor> fake_quantize_per_tensor_affine_cachemask(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) {
    return at::_ops::fake_quantize_per_tensor_affine_cachemask::call(self, scale, zero_point, quant_min, quant_max);
}

// aten::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max) -> (Tensor output, Tensor mask)
inline ::std::tuple<at::Tensor,at::Tensor> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max) {
    return at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams::call(self, scale, zero_point, fake_quant_enabled, quant_min, quant_max);
}

// aten::fake_quantize_per_tensor_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor
inline at::Tensor fake_quantize_per_tensor_affine_cachemask_backward(const at::Tensor & grad, const at::Tensor & mask) {
    return at::_ops::fake_quantize_per_tensor_affine_cachemask_backward::call(grad, mask);
}

// aten::_fake_quantize_learnable_per_tensor_affine(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor
inline at::Tensor _fake_quantize_learnable_per_tensor_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) {
    return at::_ops::_fake_quantize_learnable_per_tensor_affine::call(self, scale, zero_point, quant_min, quant_max, grad_factor);
}

// aten::_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_tensor_affine_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) {
    return at::_ops::_fake_quantize_learnable_per_tensor_affine_backward::call(grad, self, scale, zero_point, quant_min, quant_max, grad_factor);
}

// aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensor
inline at::Tensor fake_quantize_per_channel_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) {
    return at::_ops::fake_quantize_per_channel_affine::call(self, scale, zero_point, axis, quant_min, quant_max);
}

// aten::fake_quantize_per_channel_affine_cachemask(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor output, Tensor mask)
inline ::std::tuple<at::Tensor,at::Tensor> fake_quantize_per_channel_affine_cachemask(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) {
    return at::_ops::fake_quantize_per_channel_affine_cachemask::call(self, scale, zero_point, axis, quant_min, quant_max);
}

// aten::fake_quantize_per_channel_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor
inline at::Tensor fake_quantize_per_channel_affine_cachemask_backward(const at::Tensor & grad, const at::Tensor & mask) {
    return at::_ops::fake_quantize_per_channel_affine_cachemask_backward::call(grad, mask);
}

// aten::_fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor
inline at::Tensor _fake_quantize_learnable_per_channel_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) {
    return at::_ops::_fake_quantize_learnable_per_channel_affine::call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor);
}

// aten::_fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _fake_quantize_learnable_per_channel_affine_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) {
    return at::_ops::_fake_quantize_learnable_per_channel_affine_backward::call(grad, self, scale, zero_point, axis, quant_min, quant_max, grad_factor);
}

// aten::fused_moving_avg_obs_fake_quant(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> Tensor
inline at::Tensor fused_moving_avg_obs_fake_quant(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=false, bool symmetric_quant=false) {
    return at::_ops::fused_moving_avg_obs_fake_quant::call(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant);
}

// aten::_fused_moving_avg_obs_fq_helper(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask)
inline ::std::tuple<at::Tensor,at::Tensor> _fused_moving_avg_obs_fq_helper(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=false, bool symmetric_quant=false) {
    return at::_ops::_fused_moving_avg_obs_fq_helper::call(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant);
}

// aten::_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int)
inline ::std::tuple<double,int64_t> _choose_qparams_per_tensor(const at::Tensor & self, bool reduce_range=false) {
    return at::_ops::_choose_qparams_per_tensor::call(self, reduce_range);
}

// aten::_saturate_weight_to_fp16(Tensor weight) -> Tensor
inline at::Tensor _saturate_weight_to_fp16(const at::Tensor & weight) {
    return at::_ops::_saturate_weight_to_fp16::call(weight);
}

// aten::choose_qparams_optimized(Tensor input, int numel, int n_bins, float ratio, int bit_width) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> choose_qparams_optimized(const at::Tensor & input, int64_t numel, int64_t n_bins, double ratio, int64_t bit_width) {
    return at::_ops::choose_qparams_optimized::call(input, numel, n_bins, ratio, bit_width);
}

// aten::_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor _to_copy(const at::Tensor & self, at::TensorOptions options={}, bool non_blocking=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::_to_copy::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), non_blocking, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
// aten::_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor _to_copy(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, bool non_blocking, ::std::optional<at::MemoryFormat> memory_format) {
    return at::_ops::_to_copy::call(self, dtype, layout, device, pin_memory, non_blocking, memory_format);
}

// aten::meshgrid(Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> meshgrid(at::TensorList tensors) {
    return at::_ops::meshgrid::call(tensors);
}

// aten::meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[]
inline ::std::vector<at::Tensor> meshgrid(at::TensorList tensors, c10::string_view indexing) {
    return at::_ops::meshgrid_indexing::call(tensors, indexing);
}

// aten::cartesian_prod(Tensor[] tensors) -> Tensor
inline at::Tensor cartesian_prod(at::TensorList tensors) {
    return at::_ops::cartesian_prod::call(tensors);
}

// aten::combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor
inline at::Tensor combinations(const at::Tensor & self, int64_t r=2, bool with_replacement=false) {
    return at::_ops::combinations::call(self, r, with_replacement);
}

// aten::result_type.Tensor(Tensor tensor, Tensor other) -> ScalarType
inline at::ScalarType result_type(const at::Tensor & tensor, const at::Tensor & other) {
    return at::_ops::result_type_Tensor::call(tensor, other);
}

// aten::result_type.Scalar(Tensor tensor, Scalar other) -> ScalarType
inline at::ScalarType result_type(const at::Tensor & tensor, const at::Scalar & other) {
    return at::_ops::result_type_Scalar::call(tensor, other);
}

// aten::result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarType
inline at::ScalarType result_type(const at::Scalar & scalar, const at::Tensor & tensor) {
    return at::_ops::result_type_Scalar_Tensor::call(scalar, tensor);
}

// aten::result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarType
inline at::ScalarType result_type(const at::Scalar & scalar1, const at::Scalar & scalar2) {
    return at::_ops::result_type_Scalar_Scalar::call(scalar1, scalar2);
}

// aten::can_cast(ScalarType from_, ScalarType to) -> bool
inline bool can_cast(at::ScalarType from_, at::ScalarType to) {
    return at::_ops::can_cast::call(from_, to);
}

// aten::promote_types(ScalarType type1, ScalarType type2) -> ScalarType
inline at::ScalarType promote_types(at::ScalarType type1, at::ScalarType type2) {
    return at::_ops::promote_types::call(type1, type2);
}

// aten::_local_scalar_dense(Tensor self) -> Scalar
inline at::Scalar _local_scalar_dense(const at::Tensor & self) {
    return at::_ops::_local_scalar_dense::call(self);
}

// aten::_lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _lstm_mps(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
    return at::_ops::_lstm_mps::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
}

// aten::lstm_mps_backward(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[])
inline ::std::tuple<at::Tensor,::std::vector<at::Tensor>,::std::vector<at::Tensor>> lstm_mps_backward(const ::std::optional<at::Tensor> & grad_y, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
    return at::_ops::lstm_mps_backward::call(grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, layersOutputs, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
}

// aten::_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const ::std::optional<at::Tensor> & input_bias={}, const ::std::optional<at::Tensor> & hidden_bias={}) {
    return at::_ops::_thnn_fused_lstm_cell::call(input_gates, hidden_gates, cx, input_bias, hidden_bias);
}

// aten::_thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward_impl(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) {
    return at::_ops::_thnn_fused_lstm_cell_backward_impl::call(grad_hy, grad_cy, cx, cy, workspace, has_bias);
}

// aten::_thnn_fused_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) {
    return at::_ops::_thnn_fused_lstm_cell_backward::call(grad_hy, grad_cy, cx, cy, workspace, has_bias);
}

// aten::_thnn_differentiable_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor input_gates, Tensor hidden_gates, Tensor? input_bias, Tensor? hidden_bias, Tensor cx, Tensor cy) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_differentiable_lstm_cell_backward(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias, const at::Tensor & cx, const at::Tensor & cy) {
    return at::_ops::_thnn_differentiable_lstm_cell_backward::call(grad_hy, grad_cy, input_gates, hidden_gates, input_bias, hidden_bias, cx, cy);
}

// aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _thnn_fused_gru_cell(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const ::std::optional<at::Tensor> & input_bias={}, const ::std::optional<at::Tensor> & hidden_bias={}) {
    return at::_ops::_thnn_fused_gru_cell::call(input_gates, hidden_gates, hx, input_bias, hidden_bias);
}

// aten::_thnn_fused_gru_cell_backward(Tensor grad_hy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_gru_cell_backward(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias) {
    return at::_ops::_thnn_fused_gru_cell_backward::call(grad_hy, workspace, has_bias);
}

// aten::_thnn_differentiable_gru_cell_backward(Tensor grad_hy, Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias, Tensor? hidden_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_differentiable_gru_cell_backward(const at::Tensor & grad_hy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias) {
    return at::_ops::_thnn_differentiable_gru_cell_backward::call(grad_hy, input_gates, hidden_gates, hx, input_bias, hidden_bias);
}

// aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
    return at::_ops::lstm_input::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
}

// aten::lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> lstm(const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
    return at::_ops::lstm_data::call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional);
}

// aten::gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> gru(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
    return at::_ops::gru_input::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
}

// aten::gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> gru(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
    return at::_ops::gru_data::call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional);
}

// aten::rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> rnn_tanh(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
    return at::_ops::rnn_tanh_input::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
}

// aten::rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> rnn_tanh(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
    return at::_ops::rnn_tanh_data::call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional);
}

// aten::rnn_relu.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> rnn_relu(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
    return at::_ops::rnn_relu_input::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first);
}

// aten::rnn_relu.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> rnn_relu(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) {
    return at::_ops::rnn_relu_data::call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional);
}

// aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih={}, const ::std::optional<at::Tensor> & b_hh={}) {
    return at::_ops::lstm_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh);
}

// aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor
inline at::Tensor gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih={}, const ::std::optional<at::Tensor> & b_hh={}) {
    return at::_ops::gru_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh);
}

// aten::rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor
inline at::Tensor rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih={}, const ::std::optional<at::Tensor> & b_hh={}) {
    return at::_ops::rnn_tanh_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh);
}

// aten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor
inline at::Tensor rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih={}, const ::std::optional<at::Tensor> & b_hh={}) {
    return at::_ops::rnn_relu_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh);
}

// aten::quantized_lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> quantized_lstm_cell(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) {
    return at::_ops::quantized_lstm_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
}

// aten::quantized_gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
inline at::Tensor quantized_gru_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) {
    return at::_ops::quantized_gru_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
}

// aten::quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
inline at::Tensor quantized_rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) {
    return at::_ops::quantized_rnn_relu_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
}

// aten::quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
inline at::Tensor quantized_rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) {
    return at::_ops::quantized_rnn_tanh_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
}

// aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _pack_padded_sequence(const at::Tensor & input, const at::Tensor & lengths, bool batch_first) {
    return at::_ops::_pack_padded_sequence::call(input, lengths, batch_first);
}

// aten::_pack_padded_sequence_backward(Tensor grad, SymInt[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor
inline at::Tensor _pack_padded_sequence_backward(const at::Tensor & grad, at::IntArrayRef input_size, const at::Tensor & batch_sizes, bool batch_first) {
    return at::_ops::_pack_padded_sequence_backward::call(grad, c10::fromIntArrayRefSlow(input_size), batch_sizes, batch_first);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _pack_padded_sequence_backward(const at::Tensor & grad, at::IntArrayRef input_size, const at::Tensor & batch_sizes, bool batch_first) {
    return at::_ops::_pack_padded_sequence_backward::call(grad, c10::fromIntArrayRefSlow(input_size), batch_sizes, batch_first);
  }
}

// aten::_pack_padded_sequence_backward(Tensor grad, SymInt[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor
inline at::Tensor _pack_padded_sequence_backward_symint(const at::Tensor & grad, c10::SymIntArrayRef input_size, const at::Tensor & batch_sizes, bool batch_first) {
    return at::_ops::_pack_padded_sequence_backward::call(grad, input_size, batch_sizes, batch_first);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _pack_padded_sequence_backward(const at::Tensor & grad, c10::SymIntArrayRef input_size, const at::Tensor & batch_sizes, bool batch_first) {
    return at::_ops::_pack_padded_sequence_backward::call(grad, input_size, batch_sizes, batch_first);
  }
}

// aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _pad_packed_sequence(const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, const at::Scalar & padding_value, int64_t total_length) {
    return at::_ops::_pad_packed_sequence::call(data, batch_sizes, batch_first, padding_value, total_length);
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & set_(at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
    return at::_ops::set__source_Storage_storage_offset::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & set_(at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
    return at::_ops::set__source_Storage_storage_offset::call(self, source, storage_offset, size, stride);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & set_(at::Tensor & self, const at::Tensor & source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
    return at::_ops::set__source_Tensor_storage_offset::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & set_(at::Tensor & self, const at::Tensor & source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
    return at::_ops::set__source_Tensor_storage_offset::call(self, source, storage_offset, size, stride);
  }
}

// aten::lift(Tensor self) -> Tensor
inline at::Tensor lift(const at::Tensor & self) {
    return at::_ops::lift::call(self);
}

// aten::lift_fresh(Tensor(a) self) -> Tensor(a)
inline at::Tensor lift_fresh(const at::Tensor & self) {
    return at::_ops::lift_fresh::call(self);
}

// aten::lift_fresh_copy(Tensor self) -> Tensor
inline at::Tensor lift_fresh_copy(const at::Tensor & self) {
    return at::_ops::lift_fresh_copy::call(self);
}

// aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor
inline at::Tensor masked_fill(const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) {
    return at::_ops::masked_fill_Scalar::call(self, mask, value);
}

// aten::masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor
inline at::Tensor masked_fill(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) {
    return at::_ops::masked_fill_Tensor::call(self, mask, value);
}

// aten::masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor
inline at::Tensor masked_scatter(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) {
    return at::_ops::masked_scatter::call(self, mask, source);
}

// aten::masked_scatter_backward(Tensor grad_output, Tensor mask, SymInt[] sizes) -> Tensor
inline at::Tensor masked_scatter_backward(const at::Tensor & grad_output, const at::Tensor & mask, at::IntArrayRef sizes) {
    return at::_ops::masked_scatter_backward::call(grad_output, mask, c10::fromIntArrayRefSlow(sizes));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor masked_scatter_backward(const at::Tensor & grad_output, const at::Tensor & mask, at::IntArrayRef sizes) {
    return at::_ops::masked_scatter_backward::call(grad_output, mask, c10::fromIntArrayRefSlow(sizes));
  }
}

// aten::masked_scatter_backward(Tensor grad_output, Tensor mask, SymInt[] sizes) -> Tensor
inline at::Tensor masked_scatter_backward_symint(const at::Tensor & grad_output, const at::Tensor & mask, c10::SymIntArrayRef sizes) {
    return at::_ops::masked_scatter_backward::call(grad_output, mask, sizes);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor masked_scatter_backward(const at::Tensor & grad_output, const at::Tensor & mask, c10::SymIntArrayRef sizes) {
    return at::_ops::masked_scatter_backward::call(grad_output, mask, sizes);
  }
}

// aten::_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor
inline at::Tensor _masked_softmax(const at::Tensor & self, const at::Tensor & mask, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<int64_t> mask_type=::std::nullopt) {
    return at::_ops::_masked_softmax::call(self, mask, dim, mask_type);
}

// aten::_masked_softmax_backward(Tensor grad_output, Tensor output, Tensor mask, int? dim=None) -> Tensor
inline at::Tensor _masked_softmax_backward(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::_masked_softmax_backward::call(grad_output, output, mask, dim);
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor view(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::view::call(self, c10::fromIntArrayRefSlow(size));
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor view(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::view::call(self, size);
  }
}

// aten::put(Tensor self, Tensor index, Tensor source, bool accumulate=False) -> Tensor
inline at::Tensor put(const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate=false) {
    return at::_ops::put::call(self, index, source, accumulate);
}

// aten::index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_add_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha=1) {
    return at::_ops::index_add_out::call(self, dim, index, source, alpha, out);
}
// aten::index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_add_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::index_add_out::call(self, dim, index, source, alpha, out);
}

// aten::index_add(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor
inline at::Tensor index_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha=1) {
    return at::_ops::index_add::call(self, dim, index, source, alpha);
}

// aten::index_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor
inline at::Tensor index_add(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha=1) {
    return at::_ops::index_add_dimname::call(self, dim, index, source, alpha);
}

// aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_reduce_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self=true) {
    return at::_ops::index_reduce_out::call(self, dim, index, source, reduce, include_self, out);
}
// aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_reduce_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self, at::Tensor & out) {
    return at::_ops::index_reduce_out::call(self, dim, index, source, reduce, include_self, out);
}

// aten::index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor
inline at::Tensor index_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self=true) {
    return at::_ops::index_reduce::call(self, dim, index, source, reduce, include_self);
}

// aten::index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor
inline at::Tensor index_fill(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) {
    return at::_ops::index_fill_int_Scalar::call(self, dim, index, value);
}

// aten::index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor
inline at::Tensor index_fill(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) {
    return at::_ops::index_fill_int_Tensor::call(self, dim, index, value);
}

// aten::index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor
inline at::Tensor index_fill(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) {
    return at::_ops::index_fill_Dimname_Scalar::call(self, dim, index, value);
}

// aten::index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor
inline at::Tensor index_fill(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) {
    return at::_ops::index_fill_Dimname_Tensor::call(self, dim, index, value);
}

// aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor
inline at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) {
    return at::_ops::scatter_src::call(self, dim, index, src);
}

// aten::scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) {
    return at::_ops::scatter_src_out::call(self, dim, index, src, out);
}
// aten::scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) {
    return at::_ops::scatter_src_out::call(self, dim, index, src, out);
}

// aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor
inline at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) {
    return at::_ops::scatter_value::call(self, dim, index, value);
}

// aten::scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) {
    return at::_ops::scatter_value_out::call(self, dim, index, value, out);
}
// aten::scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::scatter_value_out::call(self, dim, index, value, out);
}

// aten::scatter.reduce(Tensor self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor
inline at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) {
    return at::_ops::scatter_reduce::call(self, dim, index, src, reduce);
}

// aten::scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) {
    return at::_ops::scatter_reduce_out::call(self, dim, index, src, reduce, out);
}
// aten::scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, at::Tensor & out) {
    return at::_ops::scatter_reduce_out::call(self, dim, index, src, reduce, out);
}

// aten::scatter.value_reduce(Tensor self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor
inline at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) {
    return at::_ops::scatter_value_reduce::call(self, dim, index, value, reduce);
}

// aten::scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) {
    return at::_ops::scatter_value_reduce_out::call(self, dim, index, value, reduce, out);
}
// aten::scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce, at::Tensor & out) {
    return at::_ops::scatter_value_reduce_out::call(self, dim, index, value, reduce, out);
}

// aten::scatter.dimname_src(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor
inline at::Tensor scatter(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) {
    return at::_ops::scatter_dimname_src::call(self, dim, index, src);
}

// aten::scatter.dimname_value(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor
inline at::Tensor scatter(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) {
    return at::_ops::scatter_dimname_value::call(self, dim, index, value);
}

// aten::scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor
inline at::Tensor scatter_add(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) {
    return at::_ops::scatter_add::call(self, dim, index, src);
}

// aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_add_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) {
    return at::_ops::scatter_add_out::call(self, dim, index, src, out);
}
// aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_add_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) {
    return at::_ops::scatter_add_out::call(self, dim, index, src, out);
}

// aten::scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor
inline at::Tensor scatter_add(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) {
    return at::_ops::scatter_add_dimname::call(self, dim, index, src);
}

// aten::scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor
inline at::Tensor scatter_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true) {
    return at::_ops::scatter_reduce_two::call(self, dim, index, src, reduce, include_self);
}

// aten::scatter_reduce.two_out(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_reduce_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true) {
    return at::_ops::scatter_reduce_two_out::call(self, dim, index, src, reduce, include_self, out);
}
// aten::scatter_reduce.two_out(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scatter_reduce_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self, at::Tensor & out) {
    return at::_ops::scatter_reduce_two_out::call(self, dim, index, src, reduce, include_self, out);
}

// aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_and_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_and_Tensor_out::call(self, other, out);
}
// aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_and_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_and_Tensor_out::call(self, other, out);
}

// aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_and_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_and_Scalar_out::call(self, other, out);
}
// aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_and_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::bitwise_and_Scalar_out::call(self, other, out);
}

// aten::bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor bitwise_and(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_and_Scalar::call(self, other);
}

// aten::bitwise_and.Scalar_Tensor(Scalar self, Tensor other) -> Tensor
inline at::Tensor bitwise_and(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_and_Scalar_Tensor::call(self, other);
}

// aten::bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor bitwise_and(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_and_Tensor::call(self, other);
}

// aten::__and__.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor __and__(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::__and___Scalar::call(self, other);
}

// aten::__and__.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor __and__(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::__and___Tensor::call(self, other);
}

// aten::bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_or_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_or_Tensor_out::call(self, other, out);
}
// aten::bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_or_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_or_Tensor_out::call(self, other, out);
}

// aten::bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_or_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_or_Scalar_out::call(self, other, out);
}
// aten::bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_or_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::bitwise_or_Scalar_out::call(self, other, out);
}

// aten::bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor bitwise_or(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_or_Scalar::call(self, other);
}

// aten::bitwise_or.Scalar_Tensor(Scalar self, Tensor other) -> Tensor
inline at::Tensor bitwise_or(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_or_Scalar_Tensor::call(self, other);
}

// aten::bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor bitwise_or(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_or_Tensor::call(self, other);
}

// aten::__or__.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor __or__(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::__or___Scalar::call(self, other);
}

// aten::__or__.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor __or__(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::__or___Tensor::call(self, other);
}

// aten::bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_xor_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_xor_Tensor_out::call(self, other, out);
}
// aten::bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_xor_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_xor_Tensor_out::call(self, other, out);
}

// aten::bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_xor_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_xor_Scalar_out::call(self, other, out);
}
// aten::bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_xor_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::bitwise_xor_Scalar_out::call(self, other, out);
}

// aten::bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor bitwise_xor(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_xor_Scalar::call(self, other);
}

// aten::bitwise_xor.Scalar_Tensor(Scalar self, Tensor other) -> Tensor
inline at::Tensor bitwise_xor(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_xor_Scalar_Tensor::call(self, other);
}

// aten::bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor bitwise_xor(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_xor_Tensor::call(self, other);
}

// aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor __xor__(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::__xor___Scalar::call(self, other);
}

// aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::__xor___Tensor::call(self, other);
}

// aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor __lshift__(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::__lshift___Scalar::call(self, other);
}

// aten::__lshift__.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor __lshift__(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::__lshift___Tensor::call(self, other);
}

// aten::bitwise_left_shift.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor bitwise_left_shift(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_left_shift_Tensor::call(self, other);
}

// aten::bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_left_shift_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_left_shift_Tensor_out::call(self, other, out);
}
// aten::bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_left_shift_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_left_shift_Tensor_out::call(self, other, out);
}

// aten::bitwise_left_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor bitwise_left_shift(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_left_shift_Tensor_Scalar::call(self, other);
}

// aten::bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_left_shift_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_left_shift_Tensor_Scalar_out::call(self, other, out);
}
// aten::bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_left_shift_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::bitwise_left_shift_Tensor_Scalar_out::call(self, other, out);
}

// aten::bitwise_left_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor
inline at::Tensor bitwise_left_shift(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_left_shift_Scalar_Tensor::call(self, other);
}

// aten::__rshift__.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor __rshift__(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::__rshift___Scalar::call(self, other);
}

// aten::__rshift__.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor __rshift__(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::__rshift___Tensor::call(self, other);
}

// aten::bitwise_right_shift.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor bitwise_right_shift(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_right_shift_Tensor::call(self, other);
}

// aten::bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_right_shift_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::bitwise_right_shift_Tensor_out::call(self, other, out);
}
// aten::bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_right_shift_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_right_shift_Tensor_out::call(self, other, out);
}

// aten::bitwise_right_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor bitwise_right_shift(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_right_shift_Tensor_Scalar::call(self, other);
}

// aten::bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_right_shift_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::bitwise_right_shift_Tensor_Scalar_out::call(self, other, out);
}
// aten::bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_right_shift_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::bitwise_right_shift_Tensor_Scalar_out::call(self, other, out);
}

// aten::bitwise_right_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor
inline at::Tensor bitwise_right_shift(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_right_shift_Scalar_Tensor::call(self, other);
}

// aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addbmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addbmm_out::call(self, batch1, batch2, beta, alpha, out);
}
// aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addbmm_outf(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::addbmm_out::call(self, batch1, batch2, beta, alpha, out);
}

// aten::addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
inline at::Tensor addbmm(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::addbmm::call(self, batch1, batch2, beta, alpha);
}

// aten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diag_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0) {
    return at::_ops::diag_out::call(self, diagonal, out);
}
// aten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diag_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out) {
    return at::_ops::diag_out::call(self, diagonal, out);
}

// aten::diag(Tensor self, int diagonal=0) -> Tensor
inline at::Tensor diag(const at::Tensor & self, int64_t diagonal=0) {
    return at::_ops::diag::call(self, diagonal);
}

// aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cross_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::cross_out::call(self, other, dim, out);
}
// aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cross_outf(const at::Tensor & self, const at::Tensor & other, ::std::optional<int64_t> dim, at::Tensor & out) {
    return at::_ops::cross_out::call(self, other, dim, out);
}

// aten::cross(Tensor self, Tensor other, int? dim=None) -> Tensor
inline at::Tensor cross(const at::Tensor & self, const at::Tensor & other, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::cross::call(self, other, dim);
}

// aten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & triu_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0) {
    return at::_ops::triu_out::call(self, diagonal, out);
}
// aten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & triu_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out) {
    return at::_ops::triu_out::call(self, diagonal, out);
}

// aten::triu(Tensor self, int diagonal=0) -> Tensor
inline at::Tensor triu(const at::Tensor & self, int64_t diagonal=0) {
    return at::_ops::triu::call(self, diagonal);
}

// aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tril_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0) {
    return at::_ops::tril_out::call(self, diagonal, out);
}
// aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tril_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out) {
    return at::_ops::tril_out::call(self, diagonal, out);
}

// aten::tril(Tensor self, int diagonal=0) -> Tensor
inline at::Tensor tril(const at::Tensor & self, int64_t diagonal=0) {
    return at::_ops::tril::call(self, diagonal);
}

// aten::tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong) {
    return at::_ops::tril_indices::call(row, col, offset, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::tril_indices::call(row, col, offset, dtype, layout, device, pin_memory);
}

// aten::triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong) {
    return at::_ops::triu_indices::call(row, col, offset, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::triu_indices::call(row, col, offset, dtype, layout, device, pin_memory);
}

// aten::trace(Tensor self) -> Tensor
inline at::Tensor trace(const at::Tensor & self) {
    return at::_ops::trace::call(self);
}

// aten::trace_backward(Tensor grad, SymInt[] sizes) -> Tensor
inline at::Tensor trace_backward(const at::Tensor & grad, at::IntArrayRef sizes) {
    return at::_ops::trace_backward::call(grad, c10::fromIntArrayRefSlow(sizes));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor trace_backward(const at::Tensor & grad, at::IntArrayRef sizes) {
    return at::_ops::trace_backward::call(grad, c10::fromIntArrayRefSlow(sizes));
  }
}

// aten::trace_backward(Tensor grad, SymInt[] sizes) -> Tensor
inline at::Tensor trace_backward_symint(const at::Tensor & grad, c10::SymIntArrayRef sizes) {
    return at::_ops::trace_backward::call(grad, sizes);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor trace_backward(const at::Tensor & grad, c10::SymIntArrayRef sizes) {
    return at::_ops::trace_backward::call(grad, sizes);
  }
}

// aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ne_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::ne_Scalar_out::call(self, other, out);
}
// aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ne_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::ne_Scalar_out::call(self, other, out);
}

// aten::ne.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor ne(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::ne_Scalar::call(self, other);
}

// aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ne_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::ne_Tensor_out::call(self, other, out);
}
// aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ne_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::ne_Tensor_out::call(self, other, out);
}

// aten::ne.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor ne(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::ne_Tensor::call(self, other);
}

// aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & not_equal_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::not_equal_Scalar_out::call(self, other, out);
}
// aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & not_equal_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::not_equal_Scalar_out::call(self, other, out);
}

// aten::not_equal.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor not_equal(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::not_equal_Scalar::call(self, other);
}

// aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & not_equal_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::not_equal_Tensor_out::call(self, other, out);
}
// aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & not_equal_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::not_equal_Tensor_out::call(self, other, out);
}

// aten::not_equal.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor not_equal(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::not_equal_Tensor::call(self, other);
}

// aten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eq_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::eq_Scalar_out::call(self, other, out);
}
// aten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eq_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::eq_Scalar_out::call(self, other, out);
}

// aten::eq.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor eq(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::eq_Scalar::call(self, other);
}

// aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eq_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::eq_Tensor_out::call(self, other, out);
}
// aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & eq_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::eq_Tensor_out::call(self, other, out);
}

// aten::eq.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor eq(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::eq_Tensor::call(self, other);
}

// aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ge_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::ge_Scalar_out::call(self, other, out);
}
// aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ge_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::ge_Scalar_out::call(self, other, out);
}

// aten::ge.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor ge(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::ge_Scalar::call(self, other);
}

// aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ge_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::ge_Tensor_out::call(self, other, out);
}
// aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ge_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::ge_Tensor_out::call(self, other, out);
}

// aten::ge.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor ge(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::ge_Tensor::call(self, other);
}

// aten::greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & greater_equal_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::greater_equal_Scalar_out::call(self, other, out);
}
// aten::greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & greater_equal_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::greater_equal_Scalar_out::call(self, other, out);
}

// aten::greater_equal.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor greater_equal(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::greater_equal_Scalar::call(self, other);
}

// aten::greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & greater_equal_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::greater_equal_Tensor_out::call(self, other, out);
}
// aten::greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & greater_equal_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::greater_equal_Tensor_out::call(self, other, out);
}

// aten::greater_equal.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor greater_equal(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::greater_equal_Tensor::call(self, other);
}

// aten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & le_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::le_Scalar_out::call(self, other, out);
}
// aten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & le_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::le_Scalar_out::call(self, other, out);
}

// aten::le.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor le(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::le_Scalar::call(self, other);
}

// aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & le_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::le_Tensor_out::call(self, other, out);
}
// aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & le_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::le_Tensor_out::call(self, other, out);
}

// aten::le.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor le(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::le_Tensor::call(self, other);
}

// aten::less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & less_equal_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::less_equal_Scalar_out::call(self, other, out);
}
// aten::less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & less_equal_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::less_equal_Scalar_out::call(self, other, out);
}

// aten::less_equal.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor less_equal(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::less_equal_Scalar::call(self, other);
}

// aten::less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & less_equal_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::less_equal_Tensor_out::call(self, other, out);
}
// aten::less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & less_equal_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::less_equal_Tensor_out::call(self, other, out);
}

// aten::less_equal.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor less_equal(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::less_equal_Tensor::call(self, other);
}

// aten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gt_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::gt_Scalar_out::call(self, other, out);
}
// aten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gt_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::gt_Scalar_out::call(self, other, out);
}

// aten::gt.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor gt(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::gt_Scalar::call(self, other);
}

// aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gt_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::gt_Tensor_out::call(self, other, out);
}
// aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gt_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::gt_Tensor_out::call(self, other, out);
}

// aten::gt.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor gt(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::gt_Tensor::call(self, other);
}

// aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & greater_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::greater_Scalar_out::call(self, other, out);
}
// aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & greater_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::greater_Scalar_out::call(self, other, out);
}

// aten::greater.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor greater(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::greater_Scalar::call(self, other);
}

// aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & greater_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::greater_Tensor_out::call(self, other, out);
}
// aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & greater_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::greater_Tensor_out::call(self, other, out);
}

// aten::greater.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor greater(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::greater_Tensor::call(self, other);
}

// aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lt_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::lt_Scalar_out::call(self, other, out);
}
// aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lt_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::lt_Scalar_out::call(self, other, out);
}

// aten::lt.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor lt(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::lt_Scalar::call(self, other);
}

// aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lt_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::lt_Tensor_out::call(self, other, out);
}
// aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lt_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::lt_Tensor_out::call(self, other, out);
}

// aten::lt.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor lt(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::lt_Tensor::call(self, other);
}

// aten::less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & less_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::less_Scalar_out::call(self, other, out);
}
// aten::less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & less_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::less_Scalar_out::call(self, other, out);
}

// aten::less.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor less(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::less_Scalar::call(self, other);
}

// aten::less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & less_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::less_Tensor_out::call(self, other, out);
}
// aten::less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & less_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::less_Tensor_out::call(self, other, out);
}

// aten::less.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor less(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::less_Tensor::call(self, other);
}

// aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & take_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & index) {
    return at::_ops::take_out::call(self, index, out);
}
// aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & take_outf(const at::Tensor & self, const at::Tensor & index, at::Tensor & out) {
    return at::_ops::take_out::call(self, index, out);
}

// aten::take(Tensor self, Tensor index) -> Tensor
inline at::Tensor take(const at::Tensor & self, const at::Tensor & index) {
    return at::_ops::take::call(self, index);
}

// aten::take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & take_along_dim_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::take_along_dim_out::call(self, indices, dim, out);
}
// aten::take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & take_along_dim_outf(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim, at::Tensor & out) {
    return at::_ops::take_along_dim_out::call(self, indices, dim, out);
}

// aten::take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor
inline at::Tensor take_along_dim(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::take_along_dim::call(self, indices, dim);
}

// aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_select_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index) {
    return at::_ops::index_select_out::call(self, dim, index, out);
}
// aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_select_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, at::Tensor & out) {
    return at::_ops::index_select_out::call(self, dim, index, out);
}

// aten::index_select(Tensor self, int dim, Tensor index) -> Tensor
inline at::Tensor index_select(const at::Tensor & self, int64_t dim, const at::Tensor & index) {
    return at::_ops::index_select::call(self, dim, index);
}

// aten::index_select.dimname_out(Tensor self, Dimname dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_select_out(at::Tensor & out, const at::Tensor & self, at::Dimname dim, const at::Tensor & index) {
    return at::_ops::index_select_dimname_out::call(self, dim, index, out);
}
// aten::index_select.dimname_out(Tensor self, Dimname dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_select_outf(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, at::Tensor & out) {
    return at::_ops::index_select_dimname_out::call(self, dim, index, out);
}

// aten::index_select.dimname(Tensor self, Dimname dim, Tensor index) -> Tensor
inline at::Tensor index_select(const at::Tensor & self, at::Dimname dim, const at::Tensor & index) {
    return at::_ops::index_select_dimname::call(self, dim, index);
}

// aten::index_select_backward(Tensor grad, SymInt[] self_sizes, int dim, Tensor index) -> Tensor
inline at::Tensor index_select_backward(const at::Tensor & grad, at::IntArrayRef self_sizes, int64_t dim, const at::Tensor & index) {
    return at::_ops::index_select_backward::call(grad, c10::fromIntArrayRefSlow(self_sizes), dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor index_select_backward(const at::Tensor & grad, at::IntArrayRef self_sizes, int64_t dim, const at::Tensor & index) {
    return at::_ops::index_select_backward::call(grad, c10::fromIntArrayRefSlow(self_sizes), dim, index);
  }
}

// aten::index_select_backward(Tensor grad, SymInt[] self_sizes, int dim, Tensor index) -> Tensor
inline at::Tensor index_select_backward_symint(const at::Tensor & grad, c10::SymIntArrayRef self_sizes, int64_t dim, const at::Tensor & index) {
    return at::_ops::index_select_backward::call(grad, self_sizes, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor index_select_backward(const at::Tensor & grad, c10::SymIntArrayRef self_sizes, int64_t dim, const at::Tensor & index) {
    return at::_ops::index_select_backward::call(grad, self_sizes, dim, index);
  }
}

// aten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & masked_select_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask) {
    return at::_ops::masked_select_out::call(self, mask, out);
}
// aten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & masked_select_outf(const at::Tensor & self, const at::Tensor & mask, at::Tensor & out) {
    return at::_ops::masked_select_out::call(self, mask, out);
}

// aten::masked_select(Tensor self, Tensor mask) -> Tensor
inline at::Tensor masked_select(const at::Tensor & self, const at::Tensor & mask) {
    return at::_ops::masked_select::call(self, mask);
}

// aten::masked_select_backward(Tensor grad, Tensor input, Tensor mask) -> Tensor
inline at::Tensor masked_select_backward(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & mask) {
    return at::_ops::masked_select_backward::call(grad, input, mask);
}

// aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nonzero_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::nonzero_out::call(self, out);
}
// aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nonzero_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::nonzero_out::call(self, out);
}

// aten::nonzero(Tensor self) -> Tensor
inline at::Tensor nonzero(const at::Tensor & self) {
    return at::_ops::nonzero::call(self);
}

// aten::nonzero_static.out(Tensor self, *, int size, int fill_value=-1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nonzero_static_out(at::Tensor & out, const at::Tensor & self, int64_t size, int64_t fill_value=-1) {
    return at::_ops::nonzero_static_out::call(self, size, fill_value, out);
}
// aten::nonzero_static.out(Tensor self, *, int size, int fill_value=-1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nonzero_static_outf(const at::Tensor & self, int64_t size, int64_t fill_value, at::Tensor & out) {
    return at::_ops::nonzero_static_out::call(self, size, fill_value, out);
}

// aten::nonzero_static(Tensor self, *, int size, int fill_value=-1) -> Tensor
inline at::Tensor nonzero_static(const at::Tensor & self, int64_t size, int64_t fill_value=-1) {
    return at::_ops::nonzero_static::call(self, size, fill_value);
}

// aten::nonzero_numpy(Tensor self) -> Tensor[]
inline ::std::vector<at::Tensor> nonzero_numpy(const at::Tensor & self) {
    return at::_ops::nonzero_numpy::call(self);
}

// aten::argwhere(Tensor self) -> Tensor
inline at::Tensor argwhere(const at::Tensor & self) {
    return at::_ops::argwhere::call(self);
}

// aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gather_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad=false) {
    return at::_ops::gather_out::call(self, dim, index, sparse_grad, out);
}
// aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gather_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) {
    return at::_ops::gather_out::call(self, dim, index, sparse_grad, out);
}

// aten::gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor
inline at::Tensor gather(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad=false) {
    return at::_ops::gather::call(self, dim, index, sparse_grad);
}

// aten::gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor
inline at::Tensor gather_backward(const at::Tensor & grad, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) {
    return at::_ops::gather_backward::call(grad, self, dim, index, sparse_grad);
}

// aten::gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gather_out(at::Tensor & out, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad=false) {
    return at::_ops::gather_dimname_out::call(self, dim, index, sparse_grad, out);
}
// aten::gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & gather_outf(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) {
    return at::_ops::gather_dimname_out::call(self, dim, index, sparse_grad, out);
}

// aten::gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor
inline at::Tensor gather(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad=false) {
    return at::_ops::gather_dimname::call(self, dim, index, sparse_grad);
}

// aten::_gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor
inline at::Tensor _gather_sparse_backward(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & grad) {
    return at::_ops::_gather_sparse_backward::call(self, dim, index, grad);
}

// aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addcmul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) {
    return at::_ops::addcmul_out::call(self, tensor1, tensor2, value, out);
}
// aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addcmul_outf(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::addcmul_out::call(self, tensor1, tensor2, value, out);
}

// aten::addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor
inline at::Tensor addcmul(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) {
    return at::_ops::addcmul::call(self, tensor1, tensor2, value);
}

// aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addcdiv_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) {
    return at::_ops::addcdiv_out::call(self, tensor1, tensor2, value, out);
}
// aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & addcdiv_outf(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::addcdiv_out::call(self, tensor1, tensor2, value, out);
}

// aten::addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor
inline at::Tensor addcdiv(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) {
    return at::_ops::addcdiv::call(self, tensor1, tensor2, value);
}

// aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor
inline at::Tensor cross_entropy_loss(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100, double label_smoothing=0.0) {
    return at::_ops::cross_entropy_loss::call(self, target, weight, reduction, ignore_index, label_smoothing);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor cross_entropy_loss(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100, double label_smoothing=0.0) {
    return at::_ops::cross_entropy_loss::call(self, target, weight, reduction, ignore_index, label_smoothing);
  }
}

// aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor
inline at::Tensor cross_entropy_loss_symint(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100, double label_smoothing=0.0) {
    return at::_ops::cross_entropy_loss::call(self, target, weight, reduction, ignore_index, label_smoothing);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor cross_entropy_loss(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100, double label_smoothing=0.0) {
    return at::_ops::cross_entropy_loss::call(self, target, weight, reduction, ignore_index, label_smoothing);
  }
}

// aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)
inline ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out(at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) {
    return at::_ops::triangular_solve_X::call(self, A, upper, transpose, unitriangular, X, M);
}
// aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)
inline ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_outf(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M) {
    return at::_ops::triangular_solve_X::call(self, A, upper, transpose, unitriangular, X, M);
}

// aten::triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)
inline ::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) {
    return at::_ops::triangular_solve::call(self, A, upper, transpose, unitriangular);
}

// aten::_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> ()
inline void _linalg_check_errors(const at::Tensor & info, c10::string_view api_name, bool is_matrix) {
    return at::_ops::_linalg_check_errors::call(info, api_name, is_matrix);
}

// aten::linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_solve_triangular_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & B, bool upper, bool left=true, bool unitriangular=false) {
    return at::_ops::linalg_solve_triangular_out::call(self, B, upper, left, unitriangular, out);
}
// aten::linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_solve_triangular_outf(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out) {
    return at::_ops::linalg_solve_triangular_out::call(self, B, upper, left, unitriangular, out);
}

// aten::linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor
inline at::Tensor linalg_solve_triangular(const at::Tensor & self, const at::Tensor & B, bool upper, bool left=true, bool unitriangular=false) {
    return at::_ops::linalg_solve_triangular::call(self, B, upper, left, unitriangular);
}

// aten::linalg_vander(Tensor x, *, SymInt? N=None) -> Tensor
inline at::Tensor linalg_vander(const at::Tensor & x, ::std::optional<int64_t> N=::std::nullopt) {
    return at::_ops::linalg_vander::call(x, N.has_value() ? ::std::make_optional(c10::SymInt(*N)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor linalg_vander(const at::Tensor & x, ::std::optional<int64_t> N=::std::nullopt) {
    return at::_ops::linalg_vander::call(x, N.has_value() ? ::std::make_optional(c10::SymInt(*N)) : ::std::nullopt);
  }
}

// aten::linalg_vander(Tensor x, *, SymInt? N=None) -> Tensor
inline at::Tensor linalg_vander_symint(const at::Tensor & x, ::std::optional<c10::SymInt> N=::std::nullopt) {
    return at::_ops::linalg_vander::call(x, N);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor linalg_vander(const at::Tensor & x, ::std::optional<c10::SymInt> N=::std::nullopt) {
    return at::_ops::linalg_vander::call(x, N);
  }
}

// aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & V, const at::Tensor & self, bool some=true, bool compute_uv=true) {
    return at::_ops::svd_U::call(self, some, compute_uv, U, S, V);
}
// aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_outf(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V) {
    return at::_ops::svd_U::call(self, some, compute_uv, U, S, V);
}

// aten::svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> svd(const at::Tensor & self, bool some=true, bool compute_uv=true) {
    return at::_ops::svd::call(self, some, compute_uv);
}

// aten::swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a)
inline at::Tensor swapaxes(const at::Tensor & self, int64_t axis0, int64_t axis1) {
    return at::_ops::swapaxes::call(self, axis0, axis1);
}

// aten::swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a)
inline at::Tensor swapdims(const at::Tensor & self, int64_t dim0, int64_t dim1) {
    return at::_ops::swapdims::call(self, dim0, dim1);
}

// aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cholesky_out(at::Tensor & out, const at::Tensor & self, bool upper=false) {
    return at::_ops::cholesky_out::call(self, upper, out);
}
// aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cholesky_outf(const at::Tensor & self, bool upper, at::Tensor & out) {
    return at::_ops::cholesky_out::call(self, upper, out);
}

// aten::cholesky(Tensor self, bool upper=False) -> Tensor
inline at::Tensor cholesky(const at::Tensor & self, bool upper=false) {
    return at::_ops::cholesky::call(self, upper);
}

// aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cholesky_solve_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & input2, bool upper=false) {
    return at::_ops::cholesky_solve_out::call(self, input2, upper, out);
}
// aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cholesky_solve_outf(const at::Tensor & self, const at::Tensor & input2, bool upper, at::Tensor & out) {
    return at::_ops::cholesky_solve_out::call(self, input2, upper, out);
}

// aten::cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor
inline at::Tensor cholesky_solve(const at::Tensor & self, const at::Tensor & input2, bool upper=false) {
    return at::_ops::cholesky_solve::call(self, input2, upper);
}

// aten::_cholesky_solve_helper(Tensor self, Tensor A, bool upper) -> Tensor
inline at::Tensor _cholesky_solve_helper(const at::Tensor & self, const at::Tensor & A, bool upper) {
    return at::_ops::_cholesky_solve_helper::call(self, A, upper);
}

// aten::cholesky_inverse(Tensor self, bool upper=False) -> Tensor
inline at::Tensor cholesky_inverse(const at::Tensor & self, bool upper=false) {
    return at::_ops::cholesky_inverse::call(self, upper);
}

// aten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cholesky_inverse_out(at::Tensor & out, const at::Tensor & self, bool upper=false) {
    return at::_ops::cholesky_inverse_out::call(self, upper, out);
}
// aten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cholesky_inverse_outf(const at::Tensor & self, bool upper, at::Tensor & out) {
    return at::_ops::cholesky_inverse_out::call(self, upper, out);
}

// aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)
inline ::std::tuple<at::Tensor &,at::Tensor &> qr_out(at::Tensor & Q, at::Tensor & R, const at::Tensor & self, bool some=true) {
    return at::_ops::qr_Q::call(self, some, Q, R);
}
// aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)
inline ::std::tuple<at::Tensor &,at::Tensor &> qr_outf(const at::Tensor & self, bool some, at::Tensor & Q, at::Tensor & R) {
    return at::_ops::qr_Q::call(self, some, Q, R);
}

// aten::qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R)
inline ::std::tuple<at::Tensor,at::Tensor> qr(const at::Tensor & self, bool some=true) {
    return at::_ops::qr::call(self, some);
}

// aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau)
inline ::std::tuple<at::Tensor &,at::Tensor &> geqrf_out(at::Tensor & a, at::Tensor & tau, const at::Tensor & self) {
    return at::_ops::geqrf_a::call(self, a, tau);
}
// aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau)
inline ::std::tuple<at::Tensor &,at::Tensor &> geqrf_outf(const at::Tensor & self, at::Tensor & a, at::Tensor & tau) {
    return at::_ops::geqrf_a::call(self, a, tau);
}

// aten::geqrf(Tensor self) -> (Tensor a, Tensor tau)
inline ::std::tuple<at::Tensor,at::Tensor> geqrf(const at::Tensor & self) {
    return at::_ops::geqrf::call(self);
}

// aten::orgqr(Tensor self, Tensor input2) -> Tensor
inline at::Tensor orgqr(const at::Tensor & self, const at::Tensor & input2) {
    return at::_ops::orgqr::call(self, input2);
}

// aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & orgqr_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & input2) {
    return at::_ops::orgqr_out::call(self, input2, out);
}
// aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & orgqr_outf(const at::Tensor & self, const at::Tensor & input2, at::Tensor & out) {
    return at::_ops::orgqr_out::call(self, input2, out);
}

// aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ormqr_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left=true, bool transpose=false) {
    return at::_ops::ormqr_out::call(self, input2, input3, left, transpose, out);
}
// aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ormqr_outf(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose, at::Tensor & out) {
    return at::_ops::ormqr_out::call(self, input2, input3, left, transpose, out);
}

// aten::ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor
inline at::Tensor ormqr(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left=true, bool transpose=false) {
    return at::_ops::ormqr::call(self, input2, input3, left, transpose);
}

// aten::_lu_with_info(Tensor self, bool pivot=True, bool check_errors=True) -> (Tensor LU, Tensor pivots, Tensor info)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _lu_with_info(const at::Tensor & self, bool pivot=true, bool check_errors=true) {
    return at::_ops::_lu_with_info::call(self, pivot, check_errors);
}

// aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lu_solve_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) {
    return at::_ops::lu_solve_out::call(self, LU_data, LU_pivots, out);
}
// aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lu_solve_outf(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots, at::Tensor & out) {
    return at::_ops::lu_solve_out::call(self, LU_data, LU_pivots, out);
}

// aten::lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor
inline at::Tensor lu_solve(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) {
    return at::_ops::lu_solve::call(self, LU_data, LU_pivots);
}

// aten::lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True) -> (Tensor P, Tensor L, Tensor U)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> lu_unpack(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data=true, bool unpack_pivots=true) {
    return at::_ops::lu_unpack::call(LU_data, LU_pivots, unpack_data, unpack_pivots);
}

// aten::lu_unpack.out(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> lu_unpack_out(at::Tensor & P, at::Tensor & L, at::Tensor & U, const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data=true, bool unpack_pivots=true) {
    return at::_ops::lu_unpack_out::call(LU_data, LU_pivots, unpack_data, unpack_pivots, P, L, U);
}
// aten::lu_unpack.out(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> lu_unpack_outf(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots, at::Tensor & P, at::Tensor & L, at::Tensor & U) {
    return at::_ops::lu_unpack_out::call(LU_data, LU_pivots, unpack_data, unpack_pivots, P, L, U);
}

// aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & multinomial_out(at::Tensor & out, const at::Tensor & self, int64_t num_samples, bool replacement=false, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::multinomial_out::call(self, num_samples, replacement, generator, out);
}
// aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & multinomial_outf(const at::Tensor & self, int64_t num_samples, bool replacement, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::multinomial_out::call(self, num_samples, replacement, generator, out);
}

// aten::multinomial(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor
inline at::Tensor multinomial(const at::Tensor & self, int64_t num_samples, bool replacement=false, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::multinomial::call(self, num_samples, replacement, generator);
}

// aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lgamma_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::lgamma_out::call(self, out);
}
// aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lgamma_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::lgamma_out::call(self, out);
}

// aten::lgamma(Tensor self) -> Tensor
inline at::Tensor lgamma(const at::Tensor & self) {
    return at::_ops::lgamma::call(self);
}

// aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & digamma_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::digamma_out::call(self, out);
}
// aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & digamma_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::digamma_out::call(self, out);
}

// aten::digamma(Tensor self) -> Tensor
inline at::Tensor digamma(const at::Tensor & self) {
    return at::_ops::digamma::call(self);
}

// aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & polygamma_out(at::Tensor & out, int64_t n, const at::Tensor & self) {
    return at::_ops::polygamma_out::call(n, self, out);
}
// aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & polygamma_outf(int64_t n, const at::Tensor & self, at::Tensor & out) {
    return at::_ops::polygamma_out::call(n, self, out);
}

// aten::polygamma(int n, Tensor self) -> Tensor
inline at::Tensor polygamma(int64_t n, const at::Tensor & self) {
    return at::_ops::polygamma::call(n, self);
}

// aten::erfinv(Tensor self) -> Tensor
inline at::Tensor erfinv(const at::Tensor & self) {
    return at::_ops::erfinv::call(self);
}

// aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & erfinv_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::erfinv_out::call(self, out);
}
// aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & erfinv_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::erfinv_out::call(self, out);
}

// aten::i0(Tensor self) -> Tensor
inline at::Tensor i0(const at::Tensor & self) {
    return at::_ops::i0::call(self);
}

// aten::i0_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & i0_(at::Tensor & self) {
    return at::_ops::i0_::call(self);
}

// aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & i0_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::i0_out::call(self, out);
}
// aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & i0_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::i0_out::call(self, out);
}

// aten::sign(Tensor self) -> Tensor
inline at::Tensor sign(const at::Tensor & self) {
    return at::_ops::sign::call(self);
}

// aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sign_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::sign_out::call(self, out);
}
// aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sign_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::sign_out::call(self, out);
}

// aten::signbit(Tensor self) -> Tensor
inline at::Tensor signbit(const at::Tensor & self) {
    return at::_ops::signbit::call(self);
}

// aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & signbit_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::signbit_out::call(self, out);
}
// aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & signbit_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::signbit_out::call(self, out);
}

// aten::dist(Tensor self, Tensor other, Scalar p=2) -> Tensor
inline at::Tensor dist(const at::Tensor & self, const at::Tensor & other, const at::Scalar & p=2) {
    return at::_ops::dist::call(self, other, p);
}

// aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & atan2_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::atan2_out::call(self, other, out);
}
// aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & atan2_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::atan2_out::call(self, other, out);
}

// aten::atan2(Tensor self, Tensor other) -> Tensor
inline at::Tensor atan2(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::atan2::call(self, other);
}

// aten::arctan2(Tensor self, Tensor other) -> Tensor
inline at::Tensor arctan2(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::arctan2::call(self, other);
}

// aten::arctan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arctan2_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::arctan2_out::call(self, other, out);
}
// aten::arctan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & arctan2_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::arctan2_out::call(self, other, out);
}

// aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lerp_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) {
    return at::_ops::lerp_Scalar_out::call(self, end, weight, out);
}
// aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lerp_outf(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight, at::Tensor & out) {
    return at::_ops::lerp_Scalar_out::call(self, end, weight, out);
}

// aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lerp_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) {
    return at::_ops::lerp_Tensor_out::call(self, end, weight, out);
}
// aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lerp_outf(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight, at::Tensor & out) {
    return at::_ops::lerp_Tensor_out::call(self, end, weight, out);
}

// aten::lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor
inline at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) {
    return at::_ops::lerp_Scalar::call(self, end, weight);
}

// aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor
inline at::Tensor lerp(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) {
    return at::_ops::lerp_Tensor::call(self, end, weight);
}

// aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & histc_out(at::Tensor & out, const at::Tensor & self, int64_t bins=100, const at::Scalar & min=0, const at::Scalar & max=0) {
    return at::_ops::histc_out::call(self, bins, min, max, out);
}
// aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & histc_outf(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max, at::Tensor & out) {
    return at::_ops::histc_out::call(self, bins, min, max, out);
}

// aten::histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor
inline at::Tensor histc(const at::Tensor & self, int64_t bins=100, const at::Scalar & min=0, const at::Scalar & max=0) {
    return at::_ops::histc::call(self, bins, min, max);
}

// aten::histogram.bins_tensor_out(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges)
inline ::std::tuple<at::Tensor &,at::Tensor &> histogram_out(at::Tensor & hist, at::Tensor & bin_edges, const at::Tensor & self, const at::Tensor & bins, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::histogram_bins_tensor_out::call(self, bins, weight, density, hist, bin_edges);
}
// aten::histogram.bins_tensor_out(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges)
inline ::std::tuple<at::Tensor &,at::Tensor &> histogram_outf(const at::Tensor & self, const at::Tensor & bins, const ::std::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) {
    return at::_ops::histogram_bins_tensor_out::call(self, bins, weight, density, hist, bin_edges);
}

// aten::histogram.bins_tensor(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges)
inline ::std::tuple<at::Tensor,at::Tensor> histogram(const at::Tensor & self, const at::Tensor & bins, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::histogram_bins_tensor::call(self, bins, weight, density);
}

// aten::histogram.bin_ct_out(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges)
inline ::std::tuple<at::Tensor &,at::Tensor &> histogram_out(at::Tensor & hist, at::Tensor & bin_edges, const at::Tensor & self, int64_t bins=100, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::histogram_bin_ct_out::call(self, bins, range, weight, density, hist, bin_edges);
}
// aten::histogram.bin_ct_out(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges)
inline ::std::tuple<at::Tensor &,at::Tensor &> histogram_outf(const at::Tensor & self, int64_t bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) {
    return at::_ops::histogram_bin_ct_out::call(self, bins, range, weight, density, hist, bin_edges);
}

// aten::histogram.bin_ct(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges)
inline ::std::tuple<at::Tensor,at::Tensor> histogram(const at::Tensor & self, int64_t bins=100, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::histogram_bin_ct::call(self, bins, range, weight, density);
}

// aten::_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[]
inline ::std::vector<at::Tensor> _histogramdd_bin_edges(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::_histogramdd_bin_edges::call(self, bins, range, weight, density);
}

// aten::_histogramdd_from_bin_cts(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor
inline at::Tensor _histogramdd_from_bin_cts(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::_histogramdd_from_bin_cts::call(self, bins, range, weight, density);
}

// aten::_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor
inline at::Tensor _histogramdd_from_bin_tensors(const at::Tensor & self, at::TensorList bins, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::_histogramdd_from_bin_tensors::call(self, bins, weight, density);
}

// aten::histogramdd(Tensor self, int[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)
inline ::std::tuple<at::Tensor,::std::vector<at::Tensor>> histogramdd(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::histogramdd::call(self, bins, range, weight, density);
}

// aten::histogramdd.int_bins(Tensor self, int bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)
inline ::std::tuple<at::Tensor,::std::vector<at::Tensor>> histogramdd(const at::Tensor & self, int64_t bins, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::histogramdd_int_bins::call(self, bins, range, weight, density);
}

// aten::histogramdd.TensorList_bins(Tensor self, Tensor[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges)
inline ::std::tuple<at::Tensor,::std::vector<at::Tensor>> histogramdd(const at::Tensor & self, at::TensorList bins, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::histogramdd_TensorList_bins::call(self, bins, range, weight, density);
}

// aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fmod_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::fmod_Scalar_out::call(self, other, out);
}
// aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fmod_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::fmod_Scalar_out::call(self, other, out);
}

// aten::fmod.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor fmod(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::fmod_Scalar::call(self, other);
}

// aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fmod_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::fmod_Tensor_out::call(self, other, out);
}
// aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fmod_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::fmod_Tensor_out::call(self, other, out);
}

// aten::fmod.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor fmod(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::fmod_Tensor::call(self, other);
}

// aten::hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hypot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::hypot_out::call(self, other, out);
}
// aten::hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hypot_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::hypot_out::call(self, other, out);
}

// aten::hypot(Tensor self, Tensor other) -> Tensor
inline at::Tensor hypot(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::hypot::call(self, other);
}

// aten::igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & igamma_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::igamma_out::call(self, other, out);
}
// aten::igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & igamma_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::igamma_out::call(self, other, out);
}

// aten::igamma(Tensor self, Tensor other) -> Tensor
inline at::Tensor igamma(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::igamma::call(self, other);
}

// aten::igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & igammac_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::igammac_out::call(self, other, out);
}
// aten::igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & igammac_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::igammac_out::call(self, other, out);
}

// aten::igammac(Tensor self, Tensor other) -> Tensor
inline at::Tensor igammac(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::igammac::call(self, other);
}

// aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nextafter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::nextafter_out::call(self, other, out);
}
// aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nextafter_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::nextafter_out::call(self, other, out);
}

// aten::nextafter(Tensor self, Tensor other) -> Tensor
inline at::Tensor nextafter(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::nextafter::call(self, other);
}

// aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & remainder_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::remainder_Scalar_out::call(self, other, out);
}
// aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & remainder_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::remainder_Scalar_out::call(self, other, out);
}

// aten::remainder.Scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor remainder(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::remainder_Scalar::call(self, other);
}

// aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & remainder_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::remainder_Tensor_out::call(self, other, out);
}
// aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & remainder_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::remainder_Tensor_out::call(self, other, out);
}

// aten::remainder.Tensor(Tensor self, Tensor other) -> Tensor
inline at::Tensor remainder(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::remainder_Tensor::call(self, other);
}

// aten::remainder.Scalar_Tensor(Scalar self, Tensor other) -> Tensor
inline at::Tensor remainder(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::remainder_Scalar_Tensor::call(self, other);
}

// aten::min(Tensor self) -> Tensor
inline at::Tensor min(const at::Tensor & self) {
    return at::_ops::min::call(self);
}

// aten::min.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & min_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::min_unary_out::call(self, out);
}
// aten::min.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & min_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::min_unary_out::call(self, out);
}

// aten::fmin(Tensor self, Tensor other) -> Tensor
inline at::Tensor fmin(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::fmin::call(self, other);
}

// aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fmin_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::fmin_out::call(self, other, out);
}
// aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fmin_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::fmin_out::call(self, other, out);
}

// aten::max(Tensor self) -> Tensor
inline at::Tensor max(const at::Tensor & self) {
    return at::_ops::max::call(self);
}

// aten::fmax(Tensor self, Tensor other) -> Tensor
inline at::Tensor fmax(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::fmax::call(self, other);
}

// aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fmax_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::fmax_out::call(self, other, out);
}
// aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fmax_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::fmax_out::call(self, other, out);
}

// aten::maximum(Tensor self, Tensor other) -> Tensor
inline at::Tensor maximum(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::maximum::call(self, other);
}

// aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & maximum_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::maximum_out::call(self, other, out);
}
// aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & maximum_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::maximum_out::call(self, other, out);
}

// aten::max.other(Tensor self, Tensor other) -> Tensor
inline at::Tensor max(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::max_other::call(self, other);
}

// aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::max_out::call(self, other, out);
}
// aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::max_out::call(self, other, out);
}

// aten::max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::max_unary_out::call(self, out);
}
// aten::max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::max_unary_out::call(self, out);
}

// aten::minimum(Tensor self, Tensor other) -> Tensor
inline at::Tensor minimum(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::minimum::call(self, other);
}

// aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & minimum_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::minimum_out::call(self, other, out);
}
// aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & minimum_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::minimum_out::call(self, other, out);
}

// aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & min_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::min_out::call(self, other, out);
}
// aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & min_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::min_out::call(self, other, out);
}

// aten::min.other(Tensor self, Tensor other) -> Tensor
inline at::Tensor min(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::min_other::call(self, other);
}

// aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor
inline at::Tensor quantile(const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false, c10::string_view interpolation="linear") {
    return at::_ops::quantile::call(self, q, dim, keepdim, interpolation);
}

// aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantile_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false, c10::string_view interpolation="linear") {
    return at::_ops::quantile_out::call(self, q, dim, keepdim, interpolation, out);
}
// aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantile_outf(const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) {
    return at::_ops::quantile_out::call(self, q, dim, keepdim, interpolation, out);
}

// aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor
inline at::Tensor quantile(const at::Tensor & self, double q, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false, c10::string_view interpolation="linear") {
    return at::_ops::quantile_scalar::call(self, q, dim, keepdim, interpolation);
}

// aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantile_out(at::Tensor & out, const at::Tensor & self, double q, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false, c10::string_view interpolation="linear") {
    return at::_ops::quantile_scalar_out::call(self, q, dim, keepdim, interpolation, out);
}
// aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantile_outf(const at::Tensor & self, double q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) {
    return at::_ops::quantile_scalar_out::call(self, q, dim, keepdim, interpolation, out);
}

// aten::nanquantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor
inline at::Tensor nanquantile(const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false, c10::string_view interpolation="linear") {
    return at::_ops::nanquantile::call(self, q, dim, keepdim, interpolation);
}

// aten::nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nanquantile_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false, c10::string_view interpolation="linear") {
    return at::_ops::nanquantile_out::call(self, q, dim, keepdim, interpolation, out);
}
// aten::nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nanquantile_outf(const at::Tensor & self, const at::Tensor & q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) {
    return at::_ops::nanquantile_out::call(self, q, dim, keepdim, interpolation, out);
}

// aten::nanquantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor
inline at::Tensor nanquantile(const at::Tensor & self, double q, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false, c10::string_view interpolation="linear") {
    return at::_ops::nanquantile_scalar::call(self, q, dim, keepdim, interpolation);
}

// aten::nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nanquantile_out(at::Tensor & out, const at::Tensor & self, double q, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false, c10::string_view interpolation="linear") {
    return at::_ops::nanquantile_scalar_out::call(self, q, dim, keepdim, interpolation, out);
}
// aten::nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nanquantile_outf(const at::Tensor & self, double q, ::std::optional<int64_t> dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) {
    return at::_ops::nanquantile_scalar_out::call(self, q, dim, keepdim, interpolation, out);
}

// aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim=-1, bool descending=false) {
    return at::_ops::sort_values::call(self, dim, descending, values, indices);
}
// aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> sort_outf(const at::Tensor & self, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::sort_values::call(self, dim, descending, values, indices);
}

// aten::sort.values_stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, ::std::optional<bool> stable, int64_t dim=-1, bool descending=false) {
    return at::_ops::sort_values_stable::call(self, stable, dim, descending, values, indices);
}
// aten::sort.values_stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> sort_outf(const at::Tensor & self, ::std::optional<bool> stable, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::sort_values_stable::call(self, stable, dim, descending, values, indices);
}

// aten::sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, int64_t dim=-1, bool descending=false) {
    return at::_ops::sort::call(self, dim, descending);
}

// aten::sort.stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, ::std::optional<bool> stable, int64_t dim=-1, bool descending=false) {
    return at::_ops::sort_stable::call(self, stable, dim, descending);
}

// aten::sort.dimname_values(Tensor self, Dimname dim, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool descending=false) {
    return at::_ops::sort_dimname_values::call(self, dim, descending, values, indices);
}
// aten::sort.dimname_values(Tensor self, Dimname dim, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> sort_outf(const at::Tensor & self, at::Dimname dim, bool descending, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::sort_dimname_values::call(self, dim, descending, values, indices);
}

// aten::sort.dimname_values_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> sort_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, ::std::optional<bool> stable, at::Dimname dim, bool descending=false) {
    return at::_ops::sort_dimname_values_stable::call(self, stable, dim, descending, values, indices);
}
// aten::sort.dimname_values_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> sort_outf(const at::Tensor & self, ::std::optional<bool> stable, at::Dimname dim, bool descending, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::sort_dimname_values_stable::call(self, stable, dim, descending, values, indices);
}

// aten::sort.dimname(Tensor self, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, at::Dimname dim, bool descending=false) {
    return at::_ops::sort_dimname::call(self, dim, descending);
}

// aten::sort.dimname_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, ::std::optional<bool> stable, at::Dimname dim, bool descending=false) {
    return at::_ops::sort_dimname_stable::call(self, stable, dim, descending);
}

// aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & msort_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::msort_out::call(self, out);
}
// aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & msort_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::msort_out::call(self, out);
}

// aten::msort(Tensor self) -> Tensor
inline at::Tensor msort(const at::Tensor & self) {
    return at::_ops::msort::call(self);
}

// aten::argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor
inline at::Tensor argsort(const at::Tensor & self, int64_t dim=-1, bool descending=false) {
    return at::_ops::argsort::call(self, dim, descending);
}

// aten::argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor
inline at::Tensor argsort(const at::Tensor & self, bool stable, int64_t dim=-1, bool descending=false) {
    return at::_ops::argsort_stable::call(self, stable, dim, descending);
}

// aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & argsort_out(at::Tensor & out, const at::Tensor & self, bool stable, int64_t dim=-1, bool descending=false) {
    return at::_ops::argsort_stable_out::call(self, stable, dim, descending, out);
}
// aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & argsort_outf(const at::Tensor & self, bool stable, int64_t dim, bool descending, at::Tensor & out) {
    return at::_ops::argsort_stable_out::call(self, stable, dim, descending, out);
}

// aten::argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor
inline at::Tensor argsort(const at::Tensor & self, at::Dimname dim, bool descending=false) {
    return at::_ops::argsort_dimname::call(self, dim, descending);
}

// aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) {
    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) {
    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
  }
}

// aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
  }
}

// aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) {
    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) {
    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
  }
}

// aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
inline ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_outf(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) {
    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
  }
}

// aten::topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) {
    return at::_ops::topk::call(self, k, dim, largest, sorted);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) {
    return at::_ops::topk::call(self, k, dim, largest, sorted);
  }
}

// aten::topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
inline ::std::tuple<at::Tensor,at::Tensor> topk_symint(const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) {
    return at::_ops::topk::call(self, k, dim, largest, sorted);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) {
    return at::_ops::topk::call(self, k, dim, largest, sorted);
  }
}

// aten::all(Tensor self) -> Tensor
inline at::Tensor all(const at::Tensor & self) {
    return at::_ops::all::call(self);
}

// aten::all.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & all_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::all_all_out::call(self, out);
}
// aten::all.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & all_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::all_all_out::call(self, out);
}

// aten::any(Tensor self) -> Tensor
inline at::Tensor any(const at::Tensor & self) {
    return at::_ops::any::call(self);
}

// aten::any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & any_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::any_all_out::call(self, out);
}
// aten::any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & any_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::any_all_out::call(self, out);
}

// aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & renorm_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) {
    return at::_ops::renorm_out::call(self, p, dim, maxnorm, out);
}
// aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & renorm_outf(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm, at::Tensor & out) {
    return at::_ops::renorm_out::call(self, p, dim, maxnorm, out);
}

// aten::renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor
inline at::Tensor renorm(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) {
    return at::_ops::renorm::call(self, p, dim, maxnorm);
}

// aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor
inline at::Tensor unfold_backward(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
    return at::_ops::unfold_backward::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor unfold_backward(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
    return at::_ops::unfold_backward::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step);
  }
}

// aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor
inline at::Tensor unfold_backward_symint(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
    return at::_ops::unfold_backward::call(grad_in, input_sizes, dim, size, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor unfold_backward(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
    return at::_ops::unfold_backward::call(grad_in, input_sizes, dim, size, step);
  }
}

// aten::equal(Tensor self, Tensor other) -> bool
inline bool equal(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::equal::call(self, other);
}

// aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pow_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & exponent) {
    return at::_ops::pow_Tensor_Tensor_out::call(self, exponent, out);
}
// aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pow_outf(const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out) {
    return at::_ops::pow_Tensor_Tensor_out::call(self, exponent, out);
}

// aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor
inline at::Tensor pow(const at::Tensor & self, const at::Tensor & exponent) {
    return at::_ops::pow_Tensor_Tensor::call(self, exponent);
}

// aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pow_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & exponent) {
    return at::_ops::pow_Scalar_out::call(self, exponent, out);
}
// aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pow_outf(const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out) {
    return at::_ops::pow_Scalar_out::call(self, exponent, out);
}

// aten::pow.Scalar(Scalar self, Tensor exponent) -> Tensor
inline at::Tensor pow(const at::Scalar & self, const at::Tensor & exponent) {
    return at::_ops::pow_Scalar::call(self, exponent);
}

// aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pow_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & exponent) {
    return at::_ops::pow_Tensor_Scalar_out::call(self, exponent, out);
}
// aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pow_outf(const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out) {
    return at::_ops::pow_Tensor_Scalar_out::call(self, exponent, out);
}

// aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor
inline at::Tensor pow(const at::Tensor & self, const at::Scalar & exponent) {
    return at::_ops::pow_Tensor_Scalar::call(self, exponent);
}

// aten::float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & float_power_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & exponent) {
    return at::_ops::float_power_Tensor_Tensor_out::call(self, exponent, out);
}
// aten::float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & float_power_outf(const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out) {
    return at::_ops::float_power_Tensor_Tensor_out::call(self, exponent, out);
}

// aten::float_power.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor
inline at::Tensor float_power(const at::Tensor & self, const at::Tensor & exponent) {
    return at::_ops::float_power_Tensor_Tensor::call(self, exponent);
}

// aten::float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & float_power_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & exponent) {
    return at::_ops::float_power_Scalar_out::call(self, exponent, out);
}
// aten::float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & float_power_outf(const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out) {
    return at::_ops::float_power_Scalar_out::call(self, exponent, out);
}

// aten::float_power.Scalar(Scalar self, Tensor exponent) -> Tensor
inline at::Tensor float_power(const at::Scalar & self, const at::Tensor & exponent) {
    return at::_ops::float_power_Scalar::call(self, exponent);
}

// aten::float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & float_power_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & exponent) {
    return at::_ops::float_power_Tensor_Scalar_out::call(self, exponent, out);
}
// aten::float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & float_power_outf(const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out) {
    return at::_ops::float_power_Tensor_Scalar_out::call(self, exponent, out);
}

// aten::float_power.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor
inline at::Tensor float_power(const at::Tensor & self, const at::Scalar & exponent) {
    return at::_ops::float_power_Tensor_Scalar::call(self, exponent);
}

// aten::normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor
inline at::Tensor normal_functional(const at::Tensor & self, double mean=0, double std=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_functional::call(self, mean, std, generator);
}

// aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_out(at::Tensor & out, const at::Tensor & mean, double std=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_Tensor_float_out::call(mean, std, generator, out);
}
// aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_outf(const at::Tensor & mean, double std, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::normal_Tensor_float_out::call(mean, std, generator, out);
}

// aten::normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor
inline at::Tensor normal(const at::Tensor & mean, double std=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_Tensor_float::call(mean, std, generator);
}

// aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_out(at::Tensor & out, double mean, const at::Tensor & std, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_float_Tensor_out::call(mean, std, generator, out);
}
// aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_outf(double mean, const at::Tensor & std, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::normal_float_Tensor_out::call(mean, std, generator, out);
}

// aten::normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor
inline at::Tensor normal(double mean, const at::Tensor & std, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_float_Tensor::call(mean, std, generator);
}

// aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_out(at::Tensor & out, const at::Tensor & mean, const at::Tensor & std, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_Tensor_Tensor_out::call(mean, std, generator, out);
}
// aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_outf(const at::Tensor & mean, const at::Tensor & std, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::normal_Tensor_Tensor_out::call(mean, std, generator, out);
}

// aten::normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor
inline at::Tensor normal(const at::Tensor & mean, const at::Tensor & std, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_Tensor_Tensor::call(mean, std, generator);
}

// aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor normal(double mean, double std, at::IntArrayRef size, ::std::optional<at::Generator> generator=::std::nullopt, at::TensorOptions options={}) {
    return at::_ops::normal_float_float::call(mean, std, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor normal(double mean, double std, at::IntArrayRef size, ::std::optional<at::Generator> generator=::std::nullopt, at::TensorOptions options={}) {
    return at::_ops::normal_float_float::call(mean, std, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor normal(double mean, double std, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::normal_float_float::call(mean, std, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor normal(double mean, double std, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::normal_float_float::call(mean, std, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
  }
}

// aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor normal_symint(double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator=::std::nullopt, at::TensorOptions options={}) {
    return at::_ops::normal_float_float::call(mean, std, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor normal(double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator=::std::nullopt, at::TensorOptions options={}) {
    return at::_ops::normal_float_float::call(mean, std, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
  }
}

// aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor normal_symint(double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::normal_float_float::call(mean, std, size, generator, dtype, layout, device, pin_memory);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor normal(double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::normal_float_float::call(mean, std, size, generator, dtype, layout, device, pin_memory);
  }
}

// aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_out(at::Tensor & out, double mean, double std, at::IntArrayRef size, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_float_float_out::call(mean, std, c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & normal_out(at::Tensor & out, double mean, double std, at::IntArrayRef size, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_float_float_out::call(mean, std, c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_outf(double mean, double std, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::normal_float_float_out::call(mean, std, c10::fromIntArrayRefSlow(size), generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & normal_outf(double mean, double std, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::normal_float_float_out::call(mean, std, c10::fromIntArrayRefSlow(size), generator, out);
  }
}

// aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_symint_out(at::Tensor & out, double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_float_float_out::call(mean, std, size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & normal_out(at::Tensor & out, double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_float_float_out::call(mean, std, size, generator, out);
  }
}

// aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_symint_outf(double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::normal_float_float_out::call(mean, std, size, generator, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & normal_outf(double mean, double std, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::normal_float_float_out::call(mean, std, size, generator, out);
  }
}

// aten::alias(Tensor(a) self) -> Tensor(a)
inline at::Tensor alias(const at::Tensor & self) {
    return at::_ops::alias::call(self);
}

// aten::_amp_foreach_non_finite_check_and_unscale_(Tensor(a!)[] self, Tensor(b!) found_inf, Tensor inv_scale) -> ()
inline void _amp_foreach_non_finite_check_and_unscale_(at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale) {
    return at::_ops::_amp_foreach_non_finite_check_and_unscale_::call(self, found_inf, inv_scale);
}

// aten::_amp_update_scale_(Tensor(a!) self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> Tensor(a!)
inline at::Tensor & _amp_update_scale_(at::Tensor & self, at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) {
    return at::_ops::_amp_update_scale_::call(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval);
}

// aten::_foreach_add.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_add_Scalar::call(self, scalar);
}

// aten::_foreach_add_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
inline void _foreach_add_(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_add__Scalar::call(self, scalar);
}

// aten::_foreach_add.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_add_List::call(self, other, alpha);
}

// aten::_foreach_add_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()
inline void _foreach_add_(at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_add__List::call(self, other, alpha);
}

// aten::_foreach_add.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_add_ScalarList::call(self, scalars);
}

// aten::_foreach_add_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
inline void _foreach_add_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_add__ScalarList::call(self, scalars);
}

// aten::_foreach_add.Tensor(Tensor[] self, Tensor other, *, Scalar alpha=1) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_add_Tensor::call(self, other, alpha);
}

// aten::_foreach_add_.Tensor(Tensor(a!)[] self, Tensor other, *, Scalar alpha=1) -> ()
inline void _foreach_add_(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_add__Tensor::call(self, other, alpha);
}

// aten::_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_sub(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_sub_Scalar::call(self, scalar);
}

// aten::_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
inline void _foreach_sub_(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_sub__Scalar::call(self, scalar);
}

// aten::_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_sub(at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_sub_List::call(self, other, alpha);
}

// aten::_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> ()
inline void _foreach_sub_(at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_sub__List::call(self, other, alpha);
}

// aten::_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_sub(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_sub_ScalarList::call(self, scalars);
}

// aten::_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
inline void _foreach_sub_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_sub__ScalarList::call(self, scalars);
}

// aten::_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_mul(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_mul_Scalar::call(self, scalar);
}

// aten::_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
inline void _foreach_mul_(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_mul__Scalar::call(self, scalar);
}

// aten::_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_mul(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_mul_List::call(self, other);
}

// aten::_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> ()
inline void _foreach_mul_(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_mul__List::call(self, other);
}

// aten::_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_mul(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_mul_ScalarList::call(self, scalars);
}

// aten::_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
inline void _foreach_mul_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_mul__ScalarList::call(self, scalars);
}

// aten::_foreach_mul.Tensor(Tensor[] self, Tensor other) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_mul(at::TensorList self, const at::Tensor & other) {
    return at::_ops::_foreach_mul_Tensor::call(self, other);
}

// aten::_foreach_mul_.Tensor(Tensor(a!)[] self, Tensor other) -> ()
inline void _foreach_mul_(at::TensorList self, const at::Tensor & other) {
    return at::_ops::_foreach_mul__Tensor::call(self, other);
}

// aten::_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_div(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_div_Scalar::call(self, scalar);
}

// aten::_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
inline void _foreach_div_(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_div__Scalar::call(self, scalar);
}

// aten::_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_div(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_div_List::call(self, other);
}

// aten::_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> ()
inline void _foreach_div_(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_div__List::call(self, other);
}

// aten::_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_div(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_div_ScalarList::call(self, scalars);
}

// aten::_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
inline void _foreach_div_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_div__ScalarList::call(self, scalars);
}

// aten::_foreach_div.Tensor(Tensor[] self, Tensor other) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_div(at::TensorList self, const at::Tensor & other) {
    return at::_ops::_foreach_div_Tensor::call(self, other);
}

// aten::_foreach_div_.Tensor(Tensor(a!)[] self, Tensor other) -> ()
inline void _foreach_div_(at::TensorList self, const at::Tensor & other) {
    return at::_ops::_foreach_div__Tensor::call(self, other);
}

// aten::_foreach_clamp_max.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_clamp_max_Scalar::call(self, scalar);
}

// aten::_foreach_clamp_max_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
inline void _foreach_clamp_max_(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_clamp_max__Scalar::call(self, scalar);
}

// aten::_foreach_clamp_max.List(Tensor[] self, Tensor[] other) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_clamp_max_List::call(self, other);
}

// aten::_foreach_clamp_max_.List(Tensor(a!)[] self, Tensor[] other) -> ()
inline void _foreach_clamp_max_(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_clamp_max__List::call(self, other);
}

// aten::_foreach_clamp_max.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_clamp_max(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_clamp_max_ScalarList::call(self, scalars);
}

// aten::_foreach_clamp_max_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
inline void _foreach_clamp_max_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_clamp_max__ScalarList::call(self, scalars);
}

// aten::_foreach_clamp_min.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_clamp_min_Scalar::call(self, scalar);
}

// aten::_foreach_clamp_min_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
inline void _foreach_clamp_min_(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_clamp_min__Scalar::call(self, scalar);
}

// aten::_foreach_clamp_min.List(Tensor[] self, Tensor[] other) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_clamp_min_List::call(self, other);
}

// aten::_foreach_clamp_min_.List(Tensor(a!)[] self, Tensor[] other) -> ()
inline void _foreach_clamp_min_(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_clamp_min__List::call(self, other);
}

// aten::_foreach_clamp_min.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_clamp_min(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_clamp_min_ScalarList::call(self, scalars);
}

// aten::_foreach_clamp_min_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
inline void _foreach_clamp_min_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_clamp_min__ScalarList::call(self, scalars);
}

// aten::_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_maximum_Scalar::call(self, scalar);
}

// aten::_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
inline void _foreach_maximum_(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_maximum__Scalar::call(self, scalar);
}

// aten::_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_maximum_List::call(self, other);
}

// aten::_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> ()
inline void _foreach_maximum_(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_maximum__List::call(self, other);
}

// aten::_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_maximum(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_maximum_ScalarList::call(self, scalars);
}

// aten::_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
inline void _foreach_maximum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_maximum__ScalarList::call(self, scalars);
}

// aten::_foreach_minimum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_minimum_Scalar::call(self, scalar);
}

// aten::_foreach_minimum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
inline void _foreach_minimum_(at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_minimum__Scalar::call(self, scalar);
}

// aten::_foreach_minimum.List(Tensor[] self, Tensor[] other) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_minimum_List::call(self, other);
}

// aten::_foreach_minimum_.List(Tensor(a!)[] self, Tensor[] other) -> ()
inline void _foreach_minimum_(at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_minimum__List::call(self, other);
}

// aten::_foreach_minimum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_minimum_ScalarList::call(self, scalars);
}

// aten::_foreach_minimum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
inline void _foreach_minimum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_minimum__ScalarList::call(self, scalars);
}

// aten::_foreach_addcdiv.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) {
    return at::_ops::_foreach_addcdiv_Scalar::call(self, tensor1, tensor2, value);
}

// aten::_foreach_addcdiv.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_addcdiv_ScalarList::call(self, tensor1, tensor2, scalars);
}

// aten::_foreach_addcdiv.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_addcdiv(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) {
    return at::_ops::_foreach_addcdiv_Tensor::call(self, tensor1, tensor2, scalars);
}

// aten::_foreach_addcdiv_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()
inline void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) {
    return at::_ops::_foreach_addcdiv__Scalar::call(self, tensor1, tensor2, value);
}

// aten::_foreach_addcdiv_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()
inline void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_addcdiv__ScalarList::call(self, tensor1, tensor2, scalars);
}

// aten::_foreach_addcdiv_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> ()
inline void _foreach_addcdiv_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) {
    return at::_ops::_foreach_addcdiv__Tensor::call(self, tensor1, tensor2, scalars);
}

// aten::_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) {
    return at::_ops::_foreach_addcmul_Scalar::call(self, tensor1, tensor2, value);
}

// aten::_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_addcmul_ScalarList::call(self, tensor1, tensor2, scalars);
}

// aten::_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_addcmul(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) {
    return at::_ops::_foreach_addcmul_Tensor::call(self, tensor1, tensor2, scalars);
}

// aten::_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()
inline void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) {
    return at::_ops::_foreach_addcmul__Scalar::call(self, tensor1, tensor2, value);
}

// aten::_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()
inline void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_addcmul__ScalarList::call(self, tensor1, tensor2, scalars);
}

// aten::_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> ()
inline void _foreach_addcmul_(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) {
    return at::_ops::_foreach_addcmul__Tensor::call(self, tensor1, tensor2, scalars);
}

// aten::_foreach_abs(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_abs(at::TensorList self) {
    return at::_ops::_foreach_abs::call(self);
}

// aten::_foreach_abs_(Tensor(a!)[] self) -> ()
inline void _foreach_abs_(at::TensorList self) {
    return at::_ops::_foreach_abs_::call(self);
}

// aten::_foreach_acos(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_acos(at::TensorList self) {
    return at::_ops::_foreach_acos::call(self);
}

// aten::_foreach_acos_(Tensor(a!)[] self) -> ()
inline void _foreach_acos_(at::TensorList self) {
    return at::_ops::_foreach_acos_::call(self);
}

// aten::_foreach_asin(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_asin(at::TensorList self) {
    return at::_ops::_foreach_asin::call(self);
}

// aten::_foreach_asin_(Tensor(a!)[] self) -> ()
inline void _foreach_asin_(at::TensorList self) {
    return at::_ops::_foreach_asin_::call(self);
}

// aten::_foreach_atan(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_atan(at::TensorList self) {
    return at::_ops::_foreach_atan::call(self);
}

// aten::_foreach_atan_(Tensor(a!)[] self) -> ()
inline void _foreach_atan_(at::TensorList self) {
    return at::_ops::_foreach_atan_::call(self);
}

// aten::_foreach_ceil(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_ceil(at::TensorList self) {
    return at::_ops::_foreach_ceil::call(self);
}

// aten::_foreach_ceil_(Tensor(a!)[] self) -> ()
inline void _foreach_ceil_(at::TensorList self) {
    return at::_ops::_foreach_ceil_::call(self);
}

// aten::_foreach_cos(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_cos(at::TensorList self) {
    return at::_ops::_foreach_cos::call(self);
}

// aten::_foreach_cos_(Tensor(a!)[] self) -> ()
inline void _foreach_cos_(at::TensorList self) {
    return at::_ops::_foreach_cos_::call(self);
}

// aten::_foreach_cosh(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_cosh(at::TensorList self) {
    return at::_ops::_foreach_cosh::call(self);
}

// aten::_foreach_cosh_(Tensor(a!)[] self) -> ()
inline void _foreach_cosh_(at::TensorList self) {
    return at::_ops::_foreach_cosh_::call(self);
}

// aten::_foreach_erf(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_erf(at::TensorList self) {
    return at::_ops::_foreach_erf::call(self);
}

// aten::_foreach_erf_(Tensor(a!)[] self) -> ()
inline void _foreach_erf_(at::TensorList self) {
    return at::_ops::_foreach_erf_::call(self);
}

// aten::_foreach_erfc(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_erfc(at::TensorList self) {
    return at::_ops::_foreach_erfc::call(self);
}

// aten::_foreach_erfc_(Tensor(a!)[] self) -> ()
inline void _foreach_erfc_(at::TensorList self) {
    return at::_ops::_foreach_erfc_::call(self);
}

// aten::_foreach_exp(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_exp(at::TensorList self) {
    return at::_ops::_foreach_exp::call(self);
}

// aten::_foreach_exp_(Tensor(a!)[] self) -> ()
inline void _foreach_exp_(at::TensorList self) {
    return at::_ops::_foreach_exp_::call(self);
}

// aten::_foreach_expm1(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_expm1(at::TensorList self) {
    return at::_ops::_foreach_expm1::call(self);
}

// aten::_foreach_expm1_(Tensor(a!)[] self) -> ()
inline void _foreach_expm1_(at::TensorList self) {
    return at::_ops::_foreach_expm1_::call(self);
}

// aten::_foreach_floor(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_floor(at::TensorList self) {
    return at::_ops::_foreach_floor::call(self);
}

// aten::_foreach_floor_(Tensor(a!)[] self) -> ()
inline void _foreach_floor_(at::TensorList self) {
    return at::_ops::_foreach_floor_::call(self);
}

// aten::_foreach_frac(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_frac(at::TensorList self) {
    return at::_ops::_foreach_frac::call(self);
}

// aten::_foreach_frac_(Tensor(a!)[] self) -> ()
inline void _foreach_frac_(at::TensorList self) {
    return at::_ops::_foreach_frac_::call(self);
}

// aten::_foreach_lerp.List(Tensor[] self, Tensor[] tensors1, Tensor[] weights) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_lerp(at::TensorList self, at::TensorList tensors1, at::TensorList weights) {
    return at::_ops::_foreach_lerp_List::call(self, tensors1, weights);
}

// aten::_foreach_lerp_.List(Tensor(a!)[] self, Tensor[] tensors1, Tensor[] weights) -> ()
inline void _foreach_lerp_(at::TensorList self, at::TensorList tensors1, at::TensorList weights) {
    return at::_ops::_foreach_lerp__List::call(self, tensors1, weights);
}

// aten::_foreach_lerp.Scalar(Tensor[] self, Tensor[] tensors1, Scalar weight) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_lerp(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) {
    return at::_ops::_foreach_lerp_Scalar::call(self, tensors1, weight);
}

// aten::_foreach_lerp_.Scalar(Tensor(a!)[] self, Tensor[] tensors1, Scalar weight) -> ()
inline void _foreach_lerp_(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) {
    return at::_ops::_foreach_lerp__Scalar::call(self, tensors1, weight);
}

// aten::_foreach_lerp.ScalarList(Tensor[] self, Tensor[] tensors1, Scalar[] weight) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_lerp(at::TensorList self, at::TensorList tensors1, at::ArrayRef<at::Scalar> weight) {
    return at::_ops::_foreach_lerp_ScalarList::call(self, tensors1, weight);
}

// aten::_foreach_lerp_.ScalarList(Tensor(a!)[] self, Tensor[] tensors1, Scalar[] weight) -> ()
inline void _foreach_lerp_(at::TensorList self, at::TensorList tensors1, at::ArrayRef<at::Scalar> weight) {
    return at::_ops::_foreach_lerp__ScalarList::call(self, tensors1, weight);
}

// aten::_foreach_lgamma(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_lgamma(at::TensorList self) {
    return at::_ops::_foreach_lgamma::call(self);
}

// aten::_foreach_lgamma_(Tensor(a!)[] self) -> ()
inline void _foreach_lgamma_(at::TensorList self) {
    return at::_ops::_foreach_lgamma_::call(self);
}

// aten::_foreach_log(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_log(at::TensorList self) {
    return at::_ops::_foreach_log::call(self);
}

// aten::_foreach_log_(Tensor(a!)[] self) -> ()
inline void _foreach_log_(at::TensorList self) {
    return at::_ops::_foreach_log_::call(self);
}

// aten::_foreach_log10(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_log10(at::TensorList self) {
    return at::_ops::_foreach_log10::call(self);
}

// aten::_foreach_log10_(Tensor(a!)[] self) -> ()
inline void _foreach_log10_(at::TensorList self) {
    return at::_ops::_foreach_log10_::call(self);
}

// aten::_foreach_log1p(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_log1p(at::TensorList self) {
    return at::_ops::_foreach_log1p::call(self);
}

// aten::_foreach_log1p_(Tensor(a!)[] self) -> ()
inline void _foreach_log1p_(at::TensorList self) {
    return at::_ops::_foreach_log1p_::call(self);
}

// aten::_foreach_log2(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_log2(at::TensorList self) {
    return at::_ops::_foreach_log2::call(self);
}

// aten::_foreach_log2_(Tensor(a!)[] self) -> ()
inline void _foreach_log2_(at::TensorList self) {
    return at::_ops::_foreach_log2_::call(self);
}

// aten::_foreach_max(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_max(at::TensorList self) {
    return at::_ops::_foreach_max::call(self);
}

// aten::_foreach_neg(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_neg(at::TensorList self) {
    return at::_ops::_foreach_neg::call(self);
}

// aten::_foreach_neg_(Tensor(a!)[] self) -> ()
inline void _foreach_neg_(at::TensorList self) {
    return at::_ops::_foreach_neg_::call(self);
}

// aten::_foreach_norm.Scalar(Tensor[] self, Scalar ord=2, ScalarType? dtype=None) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_norm(at::TensorList self, const at::Scalar & ord=2, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_foreach_norm_Scalar::call(self, ord, dtype);
}

// aten::_foreach_pow.List(Tensor[] self, Tensor[] exponent) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_pow(at::TensorList self, at::TensorList exponent) {
    return at::_ops::_foreach_pow_List::call(self, exponent);
}

// aten::_foreach_pow.Scalar(Tensor[] self, Scalar exponent) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_pow(at::TensorList self, const at::Scalar & exponent) {
    return at::_ops::_foreach_pow_Scalar::call(self, exponent);
}

// aten::_foreach_pow.ScalarList(Tensor[] self, Scalar[] exponent) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_pow(at::TensorList self, at::ArrayRef<at::Scalar> exponent) {
    return at::_ops::_foreach_pow_ScalarList::call(self, exponent);
}

// aten::_foreach_pow.ScalarAndTensor(Scalar self, Tensor[] exponent) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_pow(const at::Scalar & self, at::TensorList exponent) {
    return at::_ops::_foreach_pow_ScalarAndTensor::call(self, exponent);
}

// aten::_foreach_pow_.List(Tensor(a!)[] self, Tensor[] exponent) -> ()
inline void _foreach_pow_(at::TensorList self, at::TensorList exponent) {
    return at::_ops::_foreach_pow__List::call(self, exponent);
}

// aten::_foreach_pow_.Scalar(Tensor(a!)[] self, Scalar exponent) -> ()
inline void _foreach_pow_(at::TensorList self, const at::Scalar & exponent) {
    return at::_ops::_foreach_pow__Scalar::call(self, exponent);
}

// aten::_foreach_pow_.ScalarList(Tensor(a!)[] self, Scalar[] exponent) -> ()
inline void _foreach_pow_(at::TensorList self, at::ArrayRef<at::Scalar> exponent) {
    return at::_ops::_foreach_pow__ScalarList::call(self, exponent);
}

// aten::_foreach_reciprocal(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_reciprocal(at::TensorList self) {
    return at::_ops::_foreach_reciprocal::call(self);
}

// aten::_foreach_reciprocal_(Tensor(a!)[] self) -> ()
inline void _foreach_reciprocal_(at::TensorList self) {
    return at::_ops::_foreach_reciprocal_::call(self);
}

// aten::_foreach_round(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_round(at::TensorList self) {
    return at::_ops::_foreach_round::call(self);
}

// aten::_foreach_round_(Tensor(a!)[] self) -> ()
inline void _foreach_round_(at::TensorList self) {
    return at::_ops::_foreach_round_::call(self);
}

// aten::_foreach_rsqrt(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_rsqrt(at::TensorList self) {
    return at::_ops::_foreach_rsqrt::call(self);
}

// aten::_foreach_rsqrt_(Tensor(a!)[] self) -> ()
inline void _foreach_rsqrt_(at::TensorList self) {
    return at::_ops::_foreach_rsqrt_::call(self);
}

// aten::_foreach_sigmoid(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_sigmoid(at::TensorList self) {
    return at::_ops::_foreach_sigmoid::call(self);
}

// aten::_foreach_sigmoid_(Tensor(a!)[] self) -> ()
inline void _foreach_sigmoid_(at::TensorList self) {
    return at::_ops::_foreach_sigmoid_::call(self);
}

// aten::_foreach_sign(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_sign(at::TensorList self) {
    return at::_ops::_foreach_sign::call(self);
}

// aten::_foreach_sign_(Tensor(a!)[] self) -> ()
inline void _foreach_sign_(at::TensorList self) {
    return at::_ops::_foreach_sign_::call(self);
}

// aten::_foreach_sin(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_sin(at::TensorList self) {
    return at::_ops::_foreach_sin::call(self);
}

// aten::_foreach_sin_(Tensor(a!)[] self) -> ()
inline void _foreach_sin_(at::TensorList self) {
    return at::_ops::_foreach_sin_::call(self);
}

// aten::_foreach_sinh(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_sinh(at::TensorList self) {
    return at::_ops::_foreach_sinh::call(self);
}

// aten::_foreach_sinh_(Tensor(a!)[] self) -> ()
inline void _foreach_sinh_(at::TensorList self) {
    return at::_ops::_foreach_sinh_::call(self);
}

// aten::_foreach_sqrt(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_sqrt(at::TensorList self) {
    return at::_ops::_foreach_sqrt::call(self);
}

// aten::_foreach_sqrt_(Tensor(a!)[] self) -> ()
inline void _foreach_sqrt_(at::TensorList self) {
    return at::_ops::_foreach_sqrt_::call(self);
}

// aten::_foreach_tan(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_tan(at::TensorList self) {
    return at::_ops::_foreach_tan::call(self);
}

// aten::_foreach_tan_(Tensor(a!)[] self) -> ()
inline void _foreach_tan_(at::TensorList self) {
    return at::_ops::_foreach_tan_::call(self);
}

// aten::_foreach_tanh(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_tanh(at::TensorList self) {
    return at::_ops::_foreach_tanh::call(self);
}

// aten::_foreach_tanh_(Tensor(a!)[] self) -> ()
inline void _foreach_tanh_(at::TensorList self) {
    return at::_ops::_foreach_tanh_::call(self);
}

// aten::_foreach_trunc(Tensor[] self) -> Tensor[]
inline ::std::vector<at::Tensor> _foreach_trunc(at::TensorList self) {
    return at::_ops::_foreach_trunc::call(self);
}

// aten::_foreach_trunc_(Tensor(a!)[] self) -> ()
inline void _foreach_trunc_(at::TensorList self) {
    return at::_ops::_foreach_trunc_::call(self);
}

// aten::_foreach_zero_(Tensor(a!)[] self) -> ()
inline void _foreach_zero_(at::TensorList self) {
    return at::_ops::_foreach_zero_::call(self);
}

// aten::_foreach_copy_(Tensor(a!)[] self, Tensor[] src, bool non_blocking=False) -> ()
inline void _foreach_copy_(at::TensorList self, at::TensorList src, bool non_blocking=false) {
    return at::_ops::_foreach_copy_::call(self, src, non_blocking);
}

// aten::_foreach_copy(Tensor[] self, Tensor[] src, bool non_blocking=False) -> Tensor[] self_out
inline ::std::vector<at::Tensor> _foreach_copy(at::TensorList self, at::TensorList src, bool non_blocking=false) {
    return at::_ops::_foreach_copy::call(self, src, non_blocking);
}

// aten::bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor
inline at::Tensor bucketize(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32=false, bool right=false) {
    return at::_ops::bucketize_Tensor::call(self, boundaries, out_int32, right);
}

// aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bucketize_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & boundaries, bool out_int32=false, bool right=false) {
    return at::_ops::bucketize_Tensor_out::call(self, boundaries, out_int32, right, out);
}
// aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bucketize_outf(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) {
    return at::_ops::bucketize_Tensor_out::call(self, boundaries, out_int32, right, out);
}

// aten::bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor
inline at::Tensor bucketize(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32=false, bool right=false) {
    return at::_ops::bucketize_Scalar::call(self, boundaries, out_int32, right);
}

// aten::searchsorted.Tensor(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor
inline at::Tensor searchsorted(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32=false, bool right=false, ::std::optional<c10::string_view> side=::std::nullopt, const ::std::optional<at::Tensor> & sorter={}) {
    return at::_ops::searchsorted_Tensor::call(sorted_sequence, self, out_int32, right, side, sorter);
}

// aten::searchsorted.Tensor_out(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & searchsorted_out(at::Tensor & out, const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32=false, bool right=false, ::std::optional<c10::string_view> side=::std::nullopt, const ::std::optional<at::Tensor> & sorter={}) {
    return at::_ops::searchsorted_Tensor_out::call(sorted_sequence, self, out_int32, right, side, sorter, out);
}
// aten::searchsorted.Tensor_out(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & searchsorted_outf(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, ::std::optional<c10::string_view> side, const ::std::optional<at::Tensor> & sorter, at::Tensor & out) {
    return at::_ops::searchsorted_Tensor_out::call(sorted_sequence, self, out_int32, right, side, sorter, out);
}

// aten::searchsorted.Scalar(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor
inline at::Tensor searchsorted(const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32=false, bool right=false, ::std::optional<c10::string_view> side=::std::nullopt, const ::std::optional<at::Tensor> & sorter={}) {
    return at::_ops::searchsorted_Scalar::call(sorted_sequence, self, out_int32, right, side, sorter);
}

// aten::searchsorted.Scalar_out(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & searchsorted_out(at::Tensor & out, const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32=false, bool right=false, ::std::optional<c10::string_view> side=::std::nullopt, const ::std::optional<at::Tensor> & sorter={}) {
    return at::_ops::searchsorted_Scalar_out::call(sorted_sequence, self, out_int32, right, side, sorter, out);
}
// aten::searchsorted.Scalar_out(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & searchsorted_outf(const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, ::std::optional<c10::string_view> side, const ::std::optional<at::Tensor> & sorter, at::Tensor & out) {
    return at::_ops::searchsorted_Scalar_out::call(sorted_sequence, self, out_int32, right, side, sorter, out);
}

// aten::_convert_indices_from_coo_to_csr(Tensor self, int size, *, bool out_int32=False) -> Tensor
inline at::Tensor _convert_indices_from_coo_to_csr(const at::Tensor & self, int64_t size, bool out_int32=false) {
    return at::_ops::_convert_indices_from_coo_to_csr::call(self, size, out_int32);
}

// aten::_convert_indices_from_coo_to_csr.out(Tensor self, int size, *, bool out_int32=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _convert_indices_from_coo_to_csr_out(at::Tensor & out, const at::Tensor & self, int64_t size, bool out_int32=false) {
    return at::_ops::_convert_indices_from_coo_to_csr_out::call(self, size, out_int32, out);
}
// aten::_convert_indices_from_coo_to_csr.out(Tensor self, int size, *, bool out_int32=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _convert_indices_from_coo_to_csr_outf(const at::Tensor & self, int64_t size, bool out_int32, at::Tensor & out) {
    return at::_ops::_convert_indices_from_coo_to_csr_out::call(self, size, out_int32, out);
}

// aten::_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor
inline at::Tensor _convert_indices_from_csr_to_coo(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32=false, bool transpose=false) {
    return at::_ops::_convert_indices_from_csr_to_coo::call(crow_indices, col_indices, out_int32, transpose);
}

// aten::_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _convert_indices_from_csr_to_coo_out(at::Tensor & out, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32=false, bool transpose=false) {
    return at::_ops::_convert_indices_from_csr_to_coo_out::call(crow_indices, col_indices, out_int32, transpose, out);
}
// aten::_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _convert_indices_from_csr_to_coo_outf(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose, at::Tensor & out) {
    return at::_ops::_convert_indices_from_csr_to_coo_out::call(crow_indices, col_indices, out_int32, transpose, out);
}

// aten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mse_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::mse_loss_out::call(self, target, reduction, out);
}
// aten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mse_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) {
    return at::_ops::mse_loss_out::call(self, target, reduction, out);
}

// aten::mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
inline at::Tensor mse_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::mse_loss::call(self, target, reduction);
}

// aten::mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & mse_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) {
    return at::_ops::mse_loss_backward_grad_input::call(grad_output, self, target, reduction, grad_input);
}
// aten::mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & mse_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input) {
    return at::_ops::mse_loss_backward_grad_input::call(grad_output, self, target, reduction, grad_input);
}

// aten::mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
inline at::Tensor mse_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) {
    return at::_ops::mse_loss_backward::call(grad_output, self, target, reduction);
}

// aten::l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
inline at::Tensor l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::l1_loss::call(self, target, reduction);
}

// aten::multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & multi_margin_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p=1, const at::Scalar & margin=1, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::multi_margin_loss_out::call(self, target, p, margin, weight, reduction, out);
}
// aten::multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & multi_margin_loss_outf(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const ::std::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & out) {
    return at::_ops::multi_margin_loss_out::call(self, target, p, margin, weight, reduction, out);
}

// aten::multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensor
inline at::Tensor multi_margin_loss(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p=1, const at::Scalar & margin=1, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::multi_margin_loss::call(self, target, p, margin, weight, reduction);
}

// aten::multi_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & multi_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::multi_margin_loss_backward_grad_input::call(grad_output, self, target, p, margin, weight, reduction, grad_input);
}
// aten::multi_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & multi_margin_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const ::std::optional<at::Tensor> & weight, int64_t reduction, at::Tensor & grad_input) {
    return at::_ops::multi_margin_loss_backward_grad_input::call(grad_output, self, target, p, margin, weight, reduction, grad_input);
}

// aten::multi_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean) -> Tensor
inline at::Tensor multi_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::multi_margin_loss_backward::call(grad_output, self, target, p, margin, weight, reduction);
}

// aten::multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & multilabel_margin_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::multilabel_margin_loss_out::call(self, target, reduction, out);
}
// aten::multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & multilabel_margin_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) {
    return at::_ops::multilabel_margin_loss_out::call(self, target, reduction, out);
}

// aten::multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
inline at::Tensor multilabel_margin_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::multilabel_margin_loss::call(self, target, reduction);
}

// aten::multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> multilabel_margin_loss_forward_out(at::Tensor & output, at::Tensor & is_target, const at::Tensor & self, const at::Tensor & target, int64_t reduction) {
    return at::_ops::multilabel_margin_loss_forward_output::call(self, target, reduction, output, is_target);
}
// aten::multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> multilabel_margin_loss_forward_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & output, at::Tensor & is_target) {
    return at::_ops::multilabel_margin_loss_forward_output::call(self, target, reduction, output, is_target);
}

// aten::multilabel_margin_loss_forward(Tensor self, Tensor target, int reduction) -> (Tensor output, Tensor is_target)
inline ::std::tuple<at::Tensor,at::Tensor> multilabel_margin_loss_forward(const at::Tensor & self, const at::Tensor & target, int64_t reduction) {
    return at::_ops::multilabel_margin_loss_forward::call(self, target, reduction);
}

// aten::multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & multilabel_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) {
    return at::_ops::multilabel_margin_loss_backward_grad_input::call(grad_output, self, target, reduction, is_target, grad_input);
}
// aten::multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & multilabel_margin_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target, at::Tensor & grad_input) {
    return at::_ops::multilabel_margin_loss_backward_grad_input::call(grad_output, self, target, reduction, is_target, grad_input);
}

// aten::multilabel_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target) -> Tensor
inline at::Tensor multilabel_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) {
    return at::_ops::multilabel_margin_loss_backward::call(grad_output, self, target, reduction, is_target);
}

// aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nll_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss_out::call(self, target, weight, reduction, ignore_index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & nll_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss_out::call(self, target, weight, reduction, ignore_index, out);
  }
}

// aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nll_loss_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & out) {
    return at::_ops::nll_loss_out::call(self, target, weight, reduction, ignore_index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & nll_loss_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & out) {
    return at::_ops::nll_loss_out::call(self, target, weight, reduction, ignore_index, out);
  }
}

// aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nll_loss_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss_out::call(self, target, weight, reduction, ignore_index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & nll_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss_out::call(self, target, weight, reduction, ignore_index, out);
  }
}

// aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nll_loss_symint_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) {
    return at::_ops::nll_loss_out::call(self, target, weight, reduction, ignore_index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & nll_loss_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) {
    return at::_ops::nll_loss_out::call(self, target, weight, reduction, ignore_index, out);
  }
}

// aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor
inline at::Tensor nll_loss_nd(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss_nd::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor nll_loss_nd(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss_nd::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor
inline at::Tensor nll_loss_nd_symint(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss_nd::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor nll_loss_nd(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss_nd::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor
inline at::Tensor nll_loss(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor nll_loss(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor
inline at::Tensor nll_loss_symint(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor nll_loss(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) {
    return at::_ops::nll_loss_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) {
    return at::_ops::nll_loss_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
  }
}

// aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) {
    return at::_ops::nll_loss_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) {
    return at::_ops::nll_loss_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
  }
}

// aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_symint_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) {
    return at::_ops::nll_loss_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) {
    return at::_ops::nll_loss_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
  }
}

// aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_symint_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) {
    return at::_ops::nll_loss_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &> nll_loss_forward_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) {
    return at::_ops::nll_loss_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
  }
}

// aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)
inline ::std::tuple<at::Tensor,at::Tensor> nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) {
    return at::_ops::nll_loss_forward::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor> nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) {
    return at::_ops::nll_loss_forward::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)
inline ::std::tuple<at::Tensor,at::Tensor> nll_loss_forward_symint(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) {
    return at::_ops::nll_loss_forward::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor> nll_loss_forward(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) {
    return at::_ops::nll_loss_forward::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & nll_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & nll_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
  }
}

// aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & nll_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) {
    return at::_ops::nll_loss_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & nll_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) {
    return at::_ops::nll_loss_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
  }
}

// aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & nll_loss_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & nll_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
  }
}

// aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & nll_loss_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) {
    return at::_ops::nll_loss_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & nll_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) {
    return at::_ops::nll_loss_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
  }
}

// aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor
inline at::Tensor nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight);
  }
}

// aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor
inline at::Tensor nll_loss_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor nll_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight);
  }
}

// aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nll_loss2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss2d_out::call(self, target, weight, reduction, ignore_index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & nll_loss2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss2d_out::call(self, target, weight, reduction, ignore_index, out);
  }
}

// aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nll_loss2d_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & out) {
    return at::_ops::nll_loss2d_out::call(self, target, weight, reduction, ignore_index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & nll_loss2d_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & out) {
    return at::_ops::nll_loss2d_out::call(self, target, weight, reduction, ignore_index, out);
  }
}

// aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nll_loss2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss2d_out::call(self, target, weight, reduction, ignore_index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & nll_loss2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss2d_out::call(self, target, weight, reduction, ignore_index, out);
  }
}

// aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nll_loss2d_symint_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) {
    return at::_ops::nll_loss2d_out::call(self, target, weight, reduction, ignore_index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & nll_loss2d_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) {
    return at::_ops::nll_loss2d_out::call(self, target, weight, reduction, ignore_index, out);
  }
}

// aten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor
inline at::Tensor nll_loss2d(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss2d::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor nll_loss2d(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) {
    return at::_ops::nll_loss2d::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor
inline at::Tensor nll_loss2d_symint(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss2d::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor nll_loss2d(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) {
    return at::_ops::nll_loss2d::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) {
    return at::_ops::nll_loss2d_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) {
    return at::_ops::nll_loss2d_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
  }
}

// aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
  }
}

// aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_symint_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) {
    return at::_ops::nll_loss2d_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_out(at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) {
    return at::_ops::nll_loss2d_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
  }
}

// aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_symint_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &> nll_loss2d_forward_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_forward_output::call(self, target, weight, reduction, ignore_index, output, total_weight);
  }
}

// aten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)
inline ::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) {
    return at::_ops::nll_loss2d_forward::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index) {
    return at::_ops::nll_loss2d_forward::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight)
inline ::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward_symint(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) {
    return at::_ops::nll_loss2d_forward::call(self, target, weight, reduction, ignore_index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor> nll_loss2d_forward(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index) {
    return at::_ops::nll_loss2d_forward::call(self, target, weight, reduction, ignore_index);
  }
}

// aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & nll_loss2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & nll_loss2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
  }
}

// aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & nll_loss2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) {
    return at::_ops::nll_loss2d_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & nll_loss2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) {
    return at::_ops::nll_loss2d_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
  }
}

// aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & nll_loss2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & nll_loss2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
  }
}

// aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & nll_loss2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) {
    return at::_ops::nll_loss2d_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & nll_loss2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) {
    return at::_ops::nll_loss2d_backward_grad_input::call(grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input);
  }
}

// aten::nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor
inline at::Tensor nll_loss2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor nll_loss2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight);
  }
}

// aten::nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor
inline at::Tensor nll_loss2d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor nll_loss2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) {
    return at::_ops::nll_loss2d_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight);
  }
}

// aten::smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, float beta=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & smooth_l1_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double beta=1.0) {
    return at::_ops::smooth_l1_loss_out::call(self, target, reduction, beta, out);
}
// aten::smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, float beta=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & smooth_l1_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & out) {
    return at::_ops::smooth_l1_loss_out::call(self, target, reduction, beta, out);
}

// aten::smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean, float beta=1.0) -> Tensor
inline at::Tensor smooth_l1_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double beta=1.0) {
    return at::_ops::smooth_l1_loss::call(self, target, reduction, beta);
}

// aten::smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & smooth_l1_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) {
    return at::_ops::smooth_l1_loss_backward_grad_input::call(grad_output, self, target, reduction, beta, grad_input);
}
// aten::smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & smooth_l1_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & grad_input) {
    return at::_ops::smooth_l1_loss_backward_grad_input::call(grad_output, self, target, reduction, beta, grad_input);
}

// aten::smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor
inline at::Tensor smooth_l1_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) {
    return at::_ops::smooth_l1_loss_backward::call(grad_output, self, target, reduction, beta);
}

// aten::huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & huber_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double delta=1.0) {
    return at::_ops::huber_loss_out::call(self, target, reduction, delta, out);
}
// aten::huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & huber_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & out) {
    return at::_ops::huber_loss_out::call(self, target, reduction, delta, out);
}

// aten::huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor
inline at::Tensor huber_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double delta=1.0) {
    return at::_ops::huber_loss::call(self, target, reduction, delta);
}

// aten::huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & huber_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) {
    return at::_ops::huber_loss_backward_out::call(grad_output, self, target, reduction, delta, grad_input);
}
// aten::huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & huber_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & grad_input) {
    return at::_ops::huber_loss_backward_out::call(grad_output, self, target, reduction, delta, grad_input);
}

// aten::huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor
inline at::Tensor huber_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) {
    return at::_ops::huber_loss_backward::call(grad_output, self, target, reduction, delta);
}

// aten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & soft_margin_loss_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::soft_margin_loss_out::call(self, target, reduction, out);
}
// aten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & soft_margin_loss_outf(const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) {
    return at::_ops::soft_margin_loss_out::call(self, target, reduction, out);
}

// aten::soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor
inline at::Tensor soft_margin_loss(const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::soft_margin_loss::call(self, target, reduction);
}

// aten::soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & soft_margin_loss_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) {
    return at::_ops::soft_margin_loss_backward_grad_input::call(grad_output, self, target, reduction, grad_input);
}
// aten::soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & soft_margin_loss_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input) {
    return at::_ops::soft_margin_loss_backward_grad_input::call(grad_output, self, target, reduction, grad_input);
}

// aten::soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor
inline at::Tensor soft_margin_loss_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) {
    return at::_ops::soft_margin_loss_backward::call(grad_output, self, target, reduction);
}

// aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & elu_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & alpha=1, const at::Scalar & scale=1, const at::Scalar & input_scale=1) {
    return at::_ops::elu_out::call(self, alpha, scale, input_scale, out);
}
// aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & elu_outf(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, at::Tensor & out) {
    return at::_ops::elu_out::call(self, alpha, scale, input_scale, out);
}

// aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor
inline at::Tensor elu(const at::Tensor & self, const at::Scalar & alpha=1, const at::Scalar & scale=1, const at::Scalar & input_scale=1) {
    return at::_ops::elu::call(self, alpha, scale, input_scale);
}

// aten::elu_backward.grad_input(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & elu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result) {
    return at::_ops::elu_backward_grad_input::call(grad_output, alpha, scale, input_scale, is_result, self_or_result, grad_input);
}
// aten::elu_backward.grad_input(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & elu_backward_outf(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result, at::Tensor & grad_input) {
    return at::_ops::elu_backward_grad_input::call(grad_output, alpha, scale, input_scale, is_result, self_or_result, grad_input);
}

// aten::elu_backward(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result) -> Tensor
inline at::Tensor elu_backward(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result) {
    return at::_ops::elu_backward::call(grad_output, alpha, scale, input_scale, is_result, self_or_result);
}

// aten::elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!)
inline at::Tensor & elu_(at::Tensor & self, const at::Scalar & alpha=1, const at::Scalar & scale=1, const at::Scalar & input_scale=1) {
    return at::_ops::elu_::call(self, alpha, scale, input_scale);
}

// aten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & glu_out(at::Tensor & out, const at::Tensor & self, int64_t dim=-1) {
    return at::_ops::glu_out::call(self, dim, out);
}
// aten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & glu_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) {
    return at::_ops::glu_out::call(self, dim, out);
}

// aten::glu(Tensor self, int dim=-1) -> Tensor
inline at::Tensor glu(const at::Tensor & self, int64_t dim=-1) {
    return at::_ops::glu::call(self, dim);
}

// aten::glu_backward.grad_input(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & glu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) {
    return at::_ops::glu_backward_grad_input::call(grad_output, self, dim, grad_input);
}
// aten::glu_backward.grad_input(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & glu_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, at::Tensor & grad_input) {
    return at::_ops::glu_backward_grad_input::call(grad_output, self, dim, grad_input);
}

// aten::glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor
inline at::Tensor glu_backward(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) {
    return at::_ops::glu_backward::call(grad_output, self, dim);
}

// aten::glu_jvp(Tensor glu, Tensor x, Tensor dx, int dim) -> Tensor
inline at::Tensor glu_jvp(const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim) {
    return at::_ops::glu_jvp::call(glu, x, dx, dim);
}

// aten::glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor
inline at::Tensor glu_backward_jvp(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) {
    return at::_ops::glu_backward_jvp::call(grad_x, grad_glu, x, dgrad_glu, dx, dim);
}

// aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardsigmoid_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::hardsigmoid_out::call(self, out);
}
// aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardsigmoid_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::hardsigmoid_out::call(self, out);
}

// aten::hardsigmoid(Tensor self) -> Tensor
inline at::Tensor hardsigmoid(const at::Tensor & self) {
    return at::_ops::hardsigmoid::call(self);
}

// aten::hardsigmoid_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & hardsigmoid_(at::Tensor & self) {
    return at::_ops::hardsigmoid_::call(self);
}

// aten::hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & hardsigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::hardsigmoid_backward_grad_input::call(grad_output, self, grad_input);
}
// aten::hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & hardsigmoid_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) {
    return at::_ops::hardsigmoid_backward_grad_input::call(grad_output, self, grad_input);
}

// aten::hardsigmoid_backward(Tensor grad_output, Tensor self) -> Tensor
inline at::Tensor hardsigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::hardsigmoid_backward::call(grad_output, self);
}

// aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardtanh_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & min_val=-1, const at::Scalar & max_val=1) {
    return at::_ops::hardtanh_out::call(self, min_val, max_val, out);
}
// aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardtanh_outf(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & out) {
    return at::_ops::hardtanh_out::call(self, min_val, max_val, out);
}

// aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor
inline at::Tensor hardtanh(const at::Tensor & self, const at::Scalar & min_val=-1, const at::Scalar & max_val=1) {
    return at::_ops::hardtanh::call(self, min_val, max_val);
}

// aten::hardtanh_backward.grad_input(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & hardtanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) {
    return at::_ops::hardtanh_backward_grad_input::call(grad_output, self, min_val, max_val, grad_input);
}
// aten::hardtanh_backward.grad_input(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & hardtanh_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & grad_input) {
    return at::_ops::hardtanh_backward_grad_input::call(grad_output, self, min_val, max_val, grad_input);
}

// aten::hardtanh_backward(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val) -> Tensor
inline at::Tensor hardtanh_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) {
    return at::_ops::hardtanh_backward::call(grad_output, self, min_val, max_val);
}

// aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!)
inline at::Tensor & hardtanh_(at::Tensor & self, const at::Scalar & min_val=-1, const at::Scalar & max_val=1) {
    return at::_ops::hardtanh_::call(self, min_val, max_val);
}

// aten::hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardswish_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::hardswish_out::call(self, out);
}
// aten::hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardswish_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::hardswish_out::call(self, out);
}

// aten::hardswish(Tensor self) -> Tensor
inline at::Tensor hardswish(const at::Tensor & self) {
    return at::_ops::hardswish::call(self);
}

// aten::hardswish_(Tensor(a!) self) -> Tensor(a!)
inline at::Tensor & hardswish_(at::Tensor & self) {
    return at::_ops::hardswish_::call(self);
}

// aten::hardswish_backward(Tensor grad_output, Tensor self) -> Tensor
inline at::Tensor hardswish_backward(const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::hardswish_backward::call(grad_output, self);
}

// aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & leaky_relu_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & negative_slope=0.01) {
    return at::_ops::leaky_relu_out::call(self, negative_slope, out);
}
// aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & leaky_relu_outf(const at::Tensor & self, const at::Scalar & negative_slope, at::Tensor & out) {
    return at::_ops::leaky_relu_out::call(self, negative_slope, out);
}

// aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor
inline at::Tensor leaky_relu(const at::Tensor & self, const at::Scalar & negative_slope=0.01) {
    return at::_ops::leaky_relu::call(self, negative_slope);
}

// aten::leaky_relu_backward.grad_input(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & leaky_relu_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) {
    return at::_ops::leaky_relu_backward_grad_input::call(grad_output, self, negative_slope, self_is_result, grad_input);
}
// aten::leaky_relu_backward.grad_input(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & leaky_relu_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result, at::Tensor & grad_input) {
    return at::_ops::leaky_relu_backward_grad_input::call(grad_output, self, negative_slope, self_is_result, grad_input);
}

// aten::leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result) -> Tensor
inline at::Tensor leaky_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) {
    return at::_ops::leaky_relu_backward::call(grad_output, self, negative_slope, self_is_result);
}

// aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!)
inline at::Tensor & leaky_relu_(at::Tensor & self, const at::Scalar & negative_slope=0.01) {
    return at::_ops::leaky_relu_::call(self, negative_slope);
}

// aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log_sigmoid_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::log_sigmoid_out::call(self, out);
}
// aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log_sigmoid_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::log_sigmoid_out::call(self, out);
}

// aten::log_sigmoid(Tensor self) -> Tensor
inline at::Tensor log_sigmoid(const at::Tensor & self) {
    return at::_ops::log_sigmoid::call(self);
}

// aten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_out(at::Tensor & output, at::Tensor & buffer, const at::Tensor & self) {
    return at::_ops::log_sigmoid_forward_output::call(self, output, buffer);
}
// aten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> log_sigmoid_forward_outf(const at::Tensor & self, at::Tensor & output, at::Tensor & buffer) {
    return at::_ops::log_sigmoid_forward_output::call(self, output, buffer);
}

// aten::log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer)
inline ::std::tuple<at::Tensor,at::Tensor> log_sigmoid_forward(const at::Tensor & self) {
    return at::_ops::log_sigmoid_forward::call(self);
}

// aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & log_sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) {
    return at::_ops::log_sigmoid_backward_grad_input::call(grad_output, self, buffer, grad_input);
}
// aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & log_sigmoid_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer, at::Tensor & grad_input) {
    return at::_ops::log_sigmoid_backward_grad_input::call(grad_output, self, buffer, grad_input);
}

// aten::log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor
inline at::Tensor log_sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) {
    return at::_ops::log_sigmoid_backward::call(grad_output, self, buffer);
}

// aten::rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rrelu_with_noise_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::rrelu_with_noise_out::call(self, noise, lower, upper, training, generator, out);
}
// aten::rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rrelu_with_noise_outf(const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::rrelu_with_noise_out::call(self, noise, lower, upper, training, generator, out);
}

// aten::rrelu_with_noise(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor
inline at::Tensor rrelu_with_noise(const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::rrelu_with_noise::call(self, noise, lower, upper, training, generator);
}

// aten::rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result) -> Tensor
inline at::Tensor rrelu_with_noise_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result) {
    return at::_ops::rrelu_with_noise_backward::call(grad_output, self, noise, lower, upper, training, self_is_result);
}

// aten::rrelu_with_noise_(Tensor(a!) self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!)
inline at::Tensor & rrelu_with_noise_(at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::rrelu_with_noise_::call(self, noise, lower, upper, training, generator);
}

// aten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & softplus_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & beta=1, const at::Scalar & threshold=20) {
    return at::_ops::softplus_out::call(self, beta, threshold, out);
}
// aten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & softplus_outf(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & out) {
    return at::_ops::softplus_out::call(self, beta, threshold, out);
}

// aten::softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor
inline at::Tensor softplus(const at::Tensor & self, const at::Scalar & beta=1, const at::Scalar & threshold=20) {
    return at::_ops::softplus::call(self, beta, threshold);
}

// aten::softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & softplus_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) {
    return at::_ops::softplus_backward_grad_input::call(grad_output, self, beta, threshold, grad_input);
}
// aten::softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & softplus_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & grad_input) {
    return at::_ops::softplus_backward_grad_input::call(grad_output, self, beta, threshold, grad_input);
}

// aten::softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold) -> Tensor
inline at::Tensor softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) {
    return at::_ops::softplus_backward::call(grad_output, self, beta, threshold);
}

// aten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & softshrink_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & lambd=0.5) {
    return at::_ops::softshrink_out::call(self, lambd, out);
}
// aten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & softshrink_outf(const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) {
    return at::_ops::softshrink_out::call(self, lambd, out);
}

// aten::softshrink(Tensor self, Scalar lambd=0.5) -> Tensor
inline at::Tensor softshrink(const at::Tensor & self, const at::Scalar & lambd=0.5) {
    return at::_ops::softshrink::call(self, lambd);
}

// aten::softshrink_backward.grad_input(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & softshrink_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) {
    return at::_ops::softshrink_backward_grad_input::call(grad_output, self, lambd, grad_input);
}
// aten::softshrink_backward.grad_input(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & softshrink_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) {
    return at::_ops::softshrink_backward_grad_input::call(grad_output, self, lambd, grad_input);
}

// aten::softshrink_backward(Tensor grad_output, Tensor self, Scalar lambd) -> Tensor
inline at::Tensor softshrink_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) {
    return at::_ops::softshrink_backward::call(grad_output, self, lambd);
}

// aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool2d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool2d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::adaptive_avg_pool2d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & adaptive_avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::adaptive_avg_pool2d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool2d_out::call(self, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool2d_out::call(self, output_size, out);
  }
}

// aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::adaptive_avg_pool2d_out::call(self, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & adaptive_avg_pool2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::adaptive_avg_pool2d_out::call(self, output_size, out);
  }
}

// aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor
inline at::Tensor adaptive_avg_pool2d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool2d::call(self, c10::fromIntArrayRefSlow(output_size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor adaptive_avg_pool2d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool2d::call(self, c10::fromIntArrayRefSlow(output_size));
  }
}

// aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor
inline at::Tensor adaptive_avg_pool2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool2d::call(self, output_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor adaptive_avg_pool2d(const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool2d::call(self, output_size);
  }
}

// aten::mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor
inline at::Tensor mkldnn_adaptive_avg_pool2d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::mkldnn_adaptive_avg_pool2d::call(self, output_size);
}

// aten::mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::mkldnn_adaptive_avg_pool2d_out::call(self, output_size, out);
}
// aten::mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_adaptive_avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::mkldnn_adaptive_avg_pool2d_out::call(self, output_size, out);
}

// aten::mkldnn_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor
inline at::Tensor mkldnn_adaptive_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::mkldnn_adaptive_avg_pool2d_backward::call(grad_output, self);
}

// aten::_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor
inline at::Tensor _adaptive_avg_pool2d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool2d::call(self, c10::fromIntArrayRefSlow(output_size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _adaptive_avg_pool2d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool2d::call(self, c10::fromIntArrayRefSlow(output_size));
  }
}

// aten::_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor
inline at::Tensor _adaptive_avg_pool2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool2d::call(self, output_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _adaptive_avg_pool2d(const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool2d::call(self, output_size);
  }
}

// aten::_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor
inline at::Tensor _adaptive_avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::_adaptive_avg_pool2d_backward::call(grad_output, self);
}

// aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool3d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & adaptive_avg_pool3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool3d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool3d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::adaptive_avg_pool3d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & adaptive_avg_pool3d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::adaptive_avg_pool3d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool3d_out::call(self, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & adaptive_avg_pool3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool3d_out::call(self, output_size, out);
  }
}

// aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::adaptive_avg_pool3d_out::call(self, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & adaptive_avg_pool3d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::adaptive_avg_pool3d_out::call(self, output_size, out);
  }
}

// aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor
inline at::Tensor adaptive_avg_pool3d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool3d::call(self, c10::fromIntArrayRefSlow(output_size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor adaptive_avg_pool3d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool3d::call(self, c10::fromIntArrayRefSlow(output_size));
  }
}

// aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor
inline at::Tensor adaptive_avg_pool3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool3d::call(self, output_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor adaptive_avg_pool3d(const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::adaptive_avg_pool3d::call(self, output_size);
  }
}

// aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor
inline at::Tensor _adaptive_avg_pool3d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool3d::call(self, c10::fromIntArrayRefSlow(output_size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _adaptive_avg_pool3d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool3d::call(self, c10::fromIntArrayRefSlow(output_size));
  }
}

// aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor
inline at::Tensor _adaptive_avg_pool3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool3d::call(self, output_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _adaptive_avg_pool3d(const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool3d::call(self, output_size);
  }
}

// aten::adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::adaptive_avg_pool3d_backward_grad_input::call(grad_output, self, grad_input);
}
// aten::adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & adaptive_avg_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) {
    return at::_ops::adaptive_avg_pool3d_backward_grad_input::call(grad_output, self, grad_input);
}

// aten::_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor
inline at::Tensor _adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::_adaptive_avg_pool3d_backward::call(grad_output, self);
}

// aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_max_pool2d_out::call(self, output_size, out, indices);
}
// aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) {
    return at::_ops::adaptive_max_pool2d_out::call(self, output_size, out, indices);
}

// aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool2d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_max_pool2d::call(self, output_size);
}

// aten::adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & adaptive_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) {
    return at::_ops::adaptive_max_pool2d_backward_grad_input::call(grad_output, self, indices, grad_input);
}
// aten::adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & adaptive_max_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) {
    return at::_ops::adaptive_max_pool2d_backward_grad_input::call(grad_output, self, indices, grad_input);
}

// aten::adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor
inline at::Tensor adaptive_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) {
    return at::_ops::adaptive_max_pool2d_backward::call(grad_output, self, indices);
}

// aten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_max_pool3d_out::call(self, output_size, out, indices);
}
// aten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> adaptive_max_pool3d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) {
    return at::_ops::adaptive_max_pool3d_out::call(self, output_size, out, indices);
}

// aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> adaptive_max_pool3d(const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::adaptive_max_pool3d::call(self, output_size);
}

// aten::adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & adaptive_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) {
    return at::_ops::adaptive_max_pool3d_backward_grad_input::call(grad_output, self, indices, grad_input);
}
// aten::adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & adaptive_max_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) {
    return at::_ops::adaptive_max_pool3d_backward_grad_input::call(grad_output, self, indices, grad_input);
}

// aten::adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor
inline at::Tensor adaptive_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) {
    return at::_ops::adaptive_max_pool3d_backward::call(grad_output, self, indices);
}

// aten::avg_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & avg_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, ::std::optional<int64_t> divisor_override=::std::nullopt) {
    return at::_ops::avg_pool2d_out::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out);
}
// aten::avg_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override, at::Tensor & out) {
    return at::_ops::avg_pool2d_out::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out);
}

// aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
inline at::Tensor avg_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, ::std::optional<int64_t> divisor_override=::std::nullopt) {
    return at::_ops::avg_pool2d::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override);
}

// aten::avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & avg_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override) {
    return at::_ops::avg_pool2d_backward_grad_input::call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input);
}
// aten::avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & avg_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override, at::Tensor & grad_input) {
    return at::_ops::avg_pool2d_backward_grad_input::call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input);
}

// aten::avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor
inline at::Tensor avg_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override) {
    return at::_ops::avg_pool2d_backward::call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override);
}

// aten::avg_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & avg_pool3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, ::std::optional<int64_t> divisor_override=::std::nullopt) {
    return at::_ops::avg_pool3d_out::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out);
}
// aten::avg_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & avg_pool3d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override, at::Tensor & out) {
    return at::_ops::avg_pool3d_out::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out);
}

// aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor
inline at::Tensor avg_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, ::std::optional<int64_t> divisor_override=::std::nullopt) {
    return at::_ops::avg_pool3d::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override);
}

// aten::avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override) {
    return at::_ops::avg_pool3d_backward_grad_input::call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input);
}
// aten::avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & avg_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override, at::Tensor & grad_input) {
    return at::_ops::avg_pool3d_backward_grad_input::call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input);
}

// aten::avg_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor
inline at::Tensor avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, ::std::optional<int64_t> divisor_override) {
    return at::_ops::avg_pool3d_backward::call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override);
}

// aten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) {
    return at::_ops::fractional_max_pool2d_output::call(self, kernel_size, output_size, random_samples, output, indices);
}
// aten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool2d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) {
    return at::_ops::fractional_max_pool2d_output::call(self, kernel_size, output_size, random_samples, output, indices);
}

// aten::fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> fractional_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) {
    return at::_ops::fractional_max_pool2d::call(self, kernel_size, output_size, random_samples);
}

// aten::fractional_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & fractional_max_pool2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) {
    return at::_ops::fractional_max_pool2d_backward_grad_input::call(grad_output, self, kernel_size, output_size, indices, grad_input);
}
// aten::fractional_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & fractional_max_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input) {
    return at::_ops::fractional_max_pool2d_backward_grad_input::call(grad_output, self, kernel_size, output_size, indices, grad_input);
}

// aten::fractional_max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices) -> Tensor
inline at::Tensor fractional_max_pool2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) {
    return at::_ops::fractional_max_pool2d_backward::call(grad_output, self, kernel_size, output_size, indices);
}

// aten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_out(at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) {
    return at::_ops::fractional_max_pool3d_output::call(self, kernel_size, output_size, random_samples, output, indices);
}
// aten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> fractional_max_pool3d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) {
    return at::_ops::fractional_max_pool3d_output::call(self, kernel_size, output_size, random_samples, output, indices);
}

// aten::fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> fractional_max_pool3d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) {
    return at::_ops::fractional_max_pool3d::call(self, kernel_size, output_size, random_samples);
}

// aten::fractional_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & fractional_max_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) {
    return at::_ops::fractional_max_pool3d_backward_grad_input::call(grad_output, self, kernel_size, output_size, indices, grad_input);
}
// aten::fractional_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & fractional_max_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input) {
    return at::_ops::fractional_max_pool3d_backward_grad_input::call(grad_output, self, kernel_size, output_size, indices, grad_input);
}

// aten::fractional_max_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices) -> Tensor
inline at::Tensor fractional_max_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) {
    return at::_ops::fractional_max_pool3d_backward::call(grad_output, self, kernel_size, output_size, indices);
}

// aten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> max_pool2d_with_indices_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool2d_with_indices_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices);
}
// aten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> max_pool2d_with_indices_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) {
    return at::_ops::max_pool2d_with_indices_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices);
}

// aten::max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> max_pool2d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool2d_with_indices::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & max_pool2d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) {
    return at::_ops::max_pool2d_with_indices_backward_grad_input::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input);
}
// aten::max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & max_pool2d_with_indices_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices, at::Tensor & grad_input) {
    return at::_ops::max_pool2d_with_indices_backward_grad_input::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input);
}

// aten::max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices) -> Tensor
inline at::Tensor max_pool2d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) {
    return at::_ops::max_pool2d_with_indices_backward::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices);
}

// aten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> max_pool3d_with_indices_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool3d_with_indices_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices);
}
// aten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> max_pool3d_with_indices_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) {
    return at::_ops::max_pool3d_with_indices_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out, indices);
}

// aten::max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> max_pool3d_with_indices(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool3d_with_indices::call(self, kernel_size, stride, padding, dilation, ceil_mode);
}

// aten::max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & max_pool3d_with_indices_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) {
    return at::_ops::max_pool3d_with_indices_backward_grad_input::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input);
}
// aten::max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & max_pool3d_with_indices_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices, at::Tensor & grad_input) {
    return at::_ops::max_pool3d_with_indices_backward_grad_input::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input);
}

// aten::max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices) -> Tensor
inline at::Tensor max_pool3d_with_indices_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) {
    return at::_ops::max_pool3d_with_indices_backward::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices);
}

// aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_unpool2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) {
    return at::_ops::max_unpool2d_out::call(self, indices, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & max_unpool2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) {
    return at::_ops::max_unpool2d_out::call(self, indices, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_unpool2d_outf(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::max_unpool2d_out::call(self, indices, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & max_unpool2d_outf(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::max_unpool2d_out::call(self, indices, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_unpool2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size) {
    return at::_ops::max_unpool2d_out::call(self, indices, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & max_unpool2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size) {
    return at::_ops::max_unpool2d_out::call(self, indices, output_size, out);
  }
}

// aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_unpool2d_symint_outf(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::max_unpool2d_out::call(self, indices, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & max_unpool2d_outf(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::max_unpool2d_out::call(self, indices, output_size, out);
  }
}

// aten::max_unpool2d(Tensor self, Tensor indices, SymInt[2] output_size) -> Tensor
inline at::Tensor max_unpool2d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) {
    return at::_ops::max_unpool2d::call(self, indices, c10::fromIntArrayRefSlow(output_size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor max_unpool2d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) {
    return at::_ops::max_unpool2d::call(self, indices, c10::fromIntArrayRefSlow(output_size));
  }
}

// aten::max_unpool2d(Tensor self, Tensor indices, SymInt[2] output_size) -> Tensor
inline at::Tensor max_unpool2d_symint(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size) {
    return at::_ops::max_unpool2d::call(self, indices, output_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor max_unpool2d(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size) {
    return at::_ops::max_unpool2d::call(self, indices, output_size);
  }
}

// aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_unpool3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::max_unpool3d_out::call(self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & max_unpool3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::max_unpool3d_out::call(self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding, out);
  }
}

// aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_unpool3d_outf(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::max_unpool3d_out::call(self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & max_unpool3d_outf(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::max_unpool3d_out::call(self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding, out);
  }
}

// aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_unpool3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::max_unpool3d_out::call(self, indices, output_size, stride, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & max_unpool3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::max_unpool3d_out::call(self, indices, output_size, stride, padding, out);
  }
}

// aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_unpool3d_symint_outf(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::max_unpool3d_out::call(self, indices, output_size, stride, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & max_unpool3d_outf(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::max_unpool3d_out::call(self, indices, output_size, stride, padding, out);
  }
}

// aten::max_unpool3d(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding) -> Tensor
inline at::Tensor max_unpool3d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::max_unpool3d::call(self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor max_unpool3d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::max_unpool3d::call(self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding);
  }
}

// aten::max_unpool3d(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding) -> Tensor
inline at::Tensor max_unpool3d_symint(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::max_unpool3d::call(self, indices, output_size, stride, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor max_unpool3d(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::max_unpool3d::call(self, indices, output_size, stride, padding);
  }
}

// aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad1d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad1d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad1d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad1d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad1d_out::call(self, padding, out);
  }
}

// aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad1d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad1d_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad1d_out::call(self, padding, out);
  }
}

// aten::reflection_pad1d(Tensor self, SymInt[2] padding) -> Tensor
inline at::Tensor reflection_pad1d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad1d::call(self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor reflection_pad1d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad1d::call(self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::reflection_pad1d(Tensor self, SymInt[2] padding) -> Tensor
inline at::Tensor reflection_pad1d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad1d::call(self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor reflection_pad1d(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad1d::call(self, padding);
  }
}

// aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad1d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad1d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad1d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad1d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad1d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad1d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad1d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad1d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad1d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::reflection_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor
inline at::Tensor reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad1d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad1d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::reflection_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor
inline at::Tensor reflection_pad1d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad1d_backward::call(grad_output, self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor reflection_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad1d_backward::call(grad_output, self, padding);
  }
}

// aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad2d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad2d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad2d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad2d_out::call(self, padding, out);
  }
}

// aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad2d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad2d_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad2d_out::call(self, padding, out);
  }
}

// aten::reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor
inline at::Tensor reflection_pad2d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad2d::call(self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor reflection_pad2d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad2d::call(self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor
inline at::Tensor reflection_pad2d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad2d::call(self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor reflection_pad2d(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad2d::call(self, padding);
  }
}

// aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad2d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad2d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad2d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad2d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad2d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad2d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad2d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad2d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::reflection_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor
inline at::Tensor reflection_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad2d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor reflection_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad2d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::reflection_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor
inline at::Tensor reflection_pad2d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad2d_backward::call(grad_output, self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor reflection_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad2d_backward::call(grad_output, self, padding);
  }
}

// aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad3d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad3d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad3d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad3d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad3d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad3d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad3d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad3d_out::call(self, padding, out);
  }
}

// aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & reflection_pad3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad3d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad3d_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::reflection_pad3d_out::call(self, padding, out);
  }
}

// aten::reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor
inline at::Tensor reflection_pad3d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad3d::call(self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor reflection_pad3d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad3d::call(self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor
inline at::Tensor reflection_pad3d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad3d::call(self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor reflection_pad3d(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad3d::call(self, padding);
  }
}

// aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad3d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad3d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad3d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & reflection_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad3d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad3d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad3d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & reflection_pad3d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad3d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & reflection_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::reflection_pad3d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor
inline at::Tensor reflection_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad3d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor reflection_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::reflection_pad3d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor
inline at::Tensor reflection_pad3d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad3d_backward::call(grad_output, self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor reflection_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::reflection_pad3d_backward::call(grad_output, self, padding);
  }
}

// aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad1d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad1d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad1d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad1d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad1d_out::call(self, padding, out);
  }
}

// aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad1d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad1d_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad1d_out::call(self, padding, out);
  }
}

// aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor
inline at::Tensor replication_pad1d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad1d::call(self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor replication_pad1d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad1d::call(self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor
inline at::Tensor replication_pad1d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad1d::call(self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor replication_pad1d(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad1d::call(self, padding);
  }
}

// aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad1d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad1d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad1d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad1d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad1d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad1d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad1d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad1d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad1d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::replication_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor
inline at::Tensor replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad1d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad1d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::replication_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor
inline at::Tensor replication_pad1d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad1d_backward::call(grad_output, self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad1d_backward::call(grad_output, self, padding);
  }
}

// aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad2d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad2d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad2d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad2d_out::call(self, padding, out);
  }
}

// aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad2d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad2d_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad2d_out::call(self, padding, out);
  }
}

// aten::replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor
inline at::Tensor replication_pad2d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad2d::call(self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor replication_pad2d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad2d::call(self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor
inline at::Tensor replication_pad2d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad2d::call(self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor replication_pad2d(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad2d::call(self, padding);
  }
}

// aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad2d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad2d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad2d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad2d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad2d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad2d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad2d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad2d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor
inline at::Tensor replication_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad2d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor replication_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad2d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor
inline at::Tensor replication_pad2d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad2d_backward::call(grad_output, self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor replication_pad2d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad2d_backward::call(grad_output, self, padding);
  }
}

// aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad3d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad3d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad3d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad3d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad3d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad3d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad3d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad3d_out::call(self, padding, out);
  }
}

// aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & replication_pad3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad3d_out::call(self, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad3d_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::replication_pad3d_out::call(self, padding, out);
  }
}

// aten::replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor
inline at::Tensor replication_pad3d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad3d::call(self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor replication_pad3d(const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad3d::call(self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor
inline at::Tensor replication_pad3d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad3d::call(self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor replication_pad3d(const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad3d::call(self, padding);
  }
}

// aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad3d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad3d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad3d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & replication_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad3d_backward_grad_input::call(grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input);
  }
}

// aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad3d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad3d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & replication_pad3d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad3d_backward_grad_input::call(grad_output, self, padding, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & replication_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) {
    return at::_ops::replication_pad3d_backward_grad_input::call(grad_output, self, padding, grad_input);
  }
}

// aten::replication_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor
inline at::Tensor replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad3d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) {
    return at::_ops::replication_pad3d_backward::call(grad_output, self, c10::fromIntArrayRefSlow(padding));
  }
}

// aten::replication_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor
inline at::Tensor replication_pad3d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad3d_backward::call(grad_output, self, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) {
    return at::_ops::replication_pad3d_backward::call(grad_output, self, padding);
  }
}

// aten::_pad_circular(Tensor self, SymInt[] pad) -> Tensor
inline at::Tensor _pad_circular(const at::Tensor & self, at::IntArrayRef pad) {
    return at::_ops::_pad_circular::call(self, c10::fromIntArrayRefSlow(pad));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _pad_circular(const at::Tensor & self, at::IntArrayRef pad) {
    return at::_ops::_pad_circular::call(self, c10::fromIntArrayRefSlow(pad));
  }
}

// aten::_pad_circular(Tensor self, SymInt[] pad) -> Tensor
inline at::Tensor _pad_circular_symint(const at::Tensor & self, c10::SymIntArrayRef pad) {
    return at::_ops::_pad_circular::call(self, pad);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _pad_circular(const at::Tensor & self, c10::SymIntArrayRef pad) {
    return at::_ops::_pad_circular::call(self, pad);
  }
}

// aten::_pad_enum(Tensor self, SymInt[] pad, int mode, float? value=None) -> Tensor
inline at::Tensor _pad_enum(const at::Tensor & self, at::IntArrayRef pad, int64_t mode, ::std::optional<double> value=::std::nullopt) {
    return at::_ops::_pad_enum::call(self, c10::fromIntArrayRefSlow(pad), mode, value);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _pad_enum(const at::Tensor & self, at::IntArrayRef pad, int64_t mode, ::std::optional<double> value=::std::nullopt) {
    return at::_ops::_pad_enum::call(self, c10::fromIntArrayRefSlow(pad), mode, value);
  }
}

// aten::_pad_enum(Tensor self, SymInt[] pad, int mode, float? value=None) -> Tensor
inline at::Tensor _pad_enum_symint(const at::Tensor & self, c10::SymIntArrayRef pad, int64_t mode, ::std::optional<double> value=::std::nullopt) {
    return at::_ops::_pad_enum::call(self, pad, mode, value);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _pad_enum(const at::Tensor & self, c10::SymIntArrayRef pad, int64_t mode, ::std::optional<double> value=::std::nullopt) {
    return at::_ops::_pad_enum::call(self, pad, mode, value);
  }
}

// aten::pad(Tensor self, SymInt[] pad, str mode="constant", float? value=None) -> Tensor
inline at::Tensor pad(const at::Tensor & self, at::IntArrayRef pad, c10::string_view mode="constant", ::std::optional<double> value=::std::nullopt) {
    return at::_ops::pad::call(self, c10::fromIntArrayRefSlow(pad), mode, value);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor pad(const at::Tensor & self, at::IntArrayRef pad, c10::string_view mode="constant", ::std::optional<double> value=::std::nullopt) {
    return at::_ops::pad::call(self, c10::fromIntArrayRefSlow(pad), mode, value);
  }
}

// aten::pad(Tensor self, SymInt[] pad, str mode="constant", float? value=None) -> Tensor
inline at::Tensor pad_symint(const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode="constant", ::std::optional<double> value=::std::nullopt) {
    return at::_ops::pad::call(self, pad, mode, value);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor pad(const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode="constant", ::std::optional<double> value=::std::nullopt) {
    return at::_ops::pad::call(self, pad, mode, value);
  }
}

// aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_linear1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_linear1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
  }
}

// aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_linear1d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_linear1d_vec::call(input, output_size, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_linear1d_vec::call(input, output_size, align_corners, scale_factors);
  }
}

// aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_bilinear2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_bilinear2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
  }
}

// aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_bilinear2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_bilinear2d_vec::call(input, output_size, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_bilinear2d_vec::call(input, output_size, align_corners, scale_factors);
  }
}

// aten::_upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_bilinear2d_aa(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_bilinear2d_aa_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_bilinear2d_aa(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_bilinear2d_aa_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
  }
}

// aten::_upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_bilinear2d_aa_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_bilinear2d_aa_vec::call(input, output_size, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_bilinear2d_aa(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_bilinear2d_aa_vec::call(input, output_size, align_corners, scale_factors);
  }
}

// aten::upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_trilinear3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_trilinear3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
  }
}

// aten::upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_trilinear3d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_trilinear3d_vec::call(input, output_size, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_trilinear3d_vec::call(input, output_size, align_corners, scale_factors);
  }
}

// aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_bicubic2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_bicubic2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
  }
}

// aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_bicubic2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_bicubic2d_vec::call(input, output_size, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_bicubic2d_vec::call(input, output_size, align_corners, scale_factors);
  }
}

// aten::_upsample_bicubic2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_bicubic2d_aa(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_bicubic2d_aa_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_bicubic2d_aa(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_bicubic2d_aa_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
  }
}

// aten::_upsample_bicubic2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_bicubic2d_aa_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_bicubic2d_aa_vec::call(input, output_size, align_corners, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_bicubic2d_aa(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_bicubic2d_aa_vec::call(input, output_size, align_corners, scale_factors);
  }
}

// aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
  }
}

// aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_nearest1d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest1d_vec::call(input, output_size, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest1d_vec::call(input, output_size, scale_factors);
  }
}

// aten::_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_nearest_exact1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
  }
}

// aten::_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_nearest_exact1d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact1d_vec::call(input, output_size, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact1d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact1d_vec::call(input, output_size, scale_factors);
  }
}

// aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
  }
}

// aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_nearest2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest2d_vec::call(input, output_size, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest2d_vec::call(input, output_size, scale_factors);
  }
}

// aten::_upsample_nearest_exact2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_nearest_exact2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
  }
}

// aten::_upsample_nearest_exact2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_nearest_exact2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact2d_vec::call(input, output_size, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact2d_vec::call(input, output_size, scale_factors);
  }
}

// aten::upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
  }
}

// aten::upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor upsample_nearest3d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest3d_vec::call(input, output_size, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::upsample_nearest3d_vec::call(input, output_size, scale_factors);
  }
}

// aten::_upsample_nearest_exact3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_nearest_exact3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
  }
}

// aten::_upsample_nearest_exact3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
inline at::Tensor _upsample_nearest_exact3d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact3d_vec::call(input, output_size, scale_factors);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact3d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
    return at::_ops::_upsample_nearest_exact3d_vec::call(input, output_size, scale_factors);
  }
}

// aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out);
  }
}

// aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::upsample_linear1d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::upsample_linear1d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out);
  }
}

// aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_out::call(self, output_size, align_corners, scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_out::call(self, output_size, align_corners, scales, out);
  }
}

// aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::upsample_linear1d_out::call(self, output_size, align_corners, scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_linear1d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::upsample_linear1d_out::call(self, output_size, align_corners, scales, out);
  }
}

// aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor
inline at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales);
  }
}

// aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor
inline at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d::call(self, output_size, align_corners, scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_linear1d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d::call(self, output_size, align_corners, scales);
  }
}

// aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input);
  }
}

// aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input);
  }
}

// aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_linear1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales, grad_input);
  }
}

// aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_linear1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales, grad_input);
  }
}

// aten::upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor
inline at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales);
  }
}

// aten::upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor
inline at::Tensor upsample_linear1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_backward::call(grad_output, output_size, input_size, align_corners, scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_linear1d_backward::call(grad_output, output_size, input_size, align_corners, scales);
  }
}

// aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
  }
}

// aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_bilinear2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_bilinear2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
  }
}

// aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_bilinear2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
  }
}

// aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_bilinear2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_bilinear2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_bilinear2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
  }
}

// aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
  }
}

// aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_bilinear2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d::call(self, output_size, align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_bilinear2d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d::call(self, output_size, align_corners, scales_h, scales_w);
  }
}

// aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_bilinear2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_bilinear2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
  }
}

// aten::upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_bilinear2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bilinear2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
  }
}

// aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_bilinear2d_aa_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_bilinear2d_aa_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
  }
}

// aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_bilinear2d_aa_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_bilinear2d_aa_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_bilinear2d_aa_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_bilinear2d_aa_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
  }
}

// aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_bilinear2d_aa_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_out::call(self, output_size, align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_bilinear2d_aa_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_out::call(self, output_size, align_corners, scales_h, scales_w, out);
  }
}

// aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_bilinear2d_aa_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_bilinear2d_aa_out::call(self, output_size, align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_bilinear2d_aa_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_bilinear2d_aa_out::call(self, output_size, align_corners, scales_h, scales_w, out);
  }
}

// aten::_upsample_bilinear2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_bilinear2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_bilinear2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
  }
}

// aten::_upsample_bilinear2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_bilinear2d_aa_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa::call(self, output_size, align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_bilinear2d_aa(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa::call(self, output_size, align_corners, scales_h, scales_w);
  }
}

// aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_bilinear2d_aa_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_bilinear2d_aa_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_bilinear2d_aa_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_bilinear2d_aa_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_bilinear2d_aa_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_bilinear2d_aa_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_bilinear2d_aa_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_bilinear2d_aa_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_bilinear2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_bilinear2d_aa_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_bilinear2d_aa_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
  }
}

// aten::_upsample_bilinear2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_bilinear2d_aa_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_bilinear2d_aa_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bilinear2d_aa_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
  }
}

// aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
  }
}

// aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_bicubic2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_bicubic2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
  }
}

// aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_bicubic2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
  }
}

// aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_bicubic2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_bicubic2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_bicubic2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
  }
}

// aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
  }
}

// aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_bicubic2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d::call(self, output_size, align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_bicubic2d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d::call(self, output_size, align_corners, scales_h, scales_w);
  }
}

// aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_bicubic2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_bicubic2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
  }
}

// aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_bicubic2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_bicubic2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
  }
}

// aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_bicubic2d_aa_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_bicubic2d_aa_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
  }
}

// aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_bicubic2d_aa_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_bicubic2d_aa_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_bicubic2d_aa_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_bicubic2d_aa_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
  }
}

// aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_bicubic2d_aa_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_out::call(self, output_size, align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_bicubic2d_aa_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_out::call(self, output_size, align_corners, scales_h, scales_w, out);
  }
}

// aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_bicubic2d_aa_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_bicubic2d_aa_out::call(self, output_size, align_corners, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_bicubic2d_aa_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_bicubic2d_aa_out::call(self, output_size, align_corners, scales_h, scales_w, out);
  }
}

// aten::_upsample_bicubic2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_bicubic2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_bicubic2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
  }
}

// aten::_upsample_bicubic2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_bicubic2d_aa_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa::call(self, output_size, align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_bicubic2d_aa(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa::call(self, output_size, align_corners, scales_h, scales_w);
  }
}

// aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_bicubic2d_aa_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_bicubic2d_aa_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_bicubic2d_aa_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_bicubic2d_aa_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_bicubic2d_aa_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_bicubic2d_aa_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_bicubic2d_aa_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_bicubic2d_aa_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_bicubic2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_bicubic2d_aa_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_bicubic2d_aa_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
  }
}

// aten::_upsample_bicubic2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_bicubic2d_aa_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_bicubic2d_aa_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_bicubic2d_aa_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
  }
}

// aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out);
  }
}

// aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_trilinear3d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_trilinear3d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out);
  }
}

// aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_trilinear3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_out::call(self, output_size, align_corners, scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_out::call(self, output_size, align_corners, scales_d, scales_h, scales_w, out);
  }
}

// aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_trilinear3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_trilinear3d_out::call(self, output_size, align_corners, scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_trilinear3d_out::call(self, output_size, align_corners, scales_d, scales_h, scales_w, out);
  }
}

// aten::upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w);
  }
}

// aten::upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_trilinear3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d::call(self, output_size, align_corners, scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_trilinear3d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d::call(self, output_size, align_corners, scales_d, scales_h, scales_w);
  }
}

// aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_trilinear3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_trilinear3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w);
  }
}

// aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_trilinear3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_backward::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_trilinear3d_backward::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w);
  }
}

// aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
  }
}

// aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::upsample_nearest1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::upsample_nearest1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
  }
}

// aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_out::call(self, output_size, scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_out::call(self, output_size, scales, out);
  }
}

// aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::upsample_nearest1d_out::call(self, output_size, scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::upsample_nearest1d_out::call(self, output_size, scales, out);
  }
}

// aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
  }
}

// aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
  }
}

// aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_out::call(self, output_size, scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact1d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_out::call(self, output_size, scales, out);
  }
}

// aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact1d_out::call(self, output_size, scales, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact1d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact1d_out::call(self, output_size, scales, out);
  }
}

// aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor
inline at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d::call(self, c10::fromIntArrayRefSlow(output_size), scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d::call(self, c10::fromIntArrayRefSlow(output_size), scales);
  }
}

// aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor
inline at::Tensor upsample_nearest1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d::call(self, output_size, scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest1d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d::call(self, output_size, scales);
  }
}

// aten::_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor
inline at::Tensor _upsample_nearest_exact1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d::call(self, c10::fromIntArrayRefSlow(output_size), scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d::call(self, c10::fromIntArrayRefSlow(output_size), scales);
  }
}

// aten::_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor
inline at::Tensor _upsample_nearest_exact1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d::call(self, output_size, scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact1d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d::call(self, output_size, scales);
  }
}

// aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
  }
}

// aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
  }
}

// aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
  }
}

// aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
  }
}

// aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
  }
}

// aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
  }
}

// aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
  }
}

// aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact1d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
  }
}

// aten::upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor
inline at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales);
  }
}

// aten::upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor
inline at::Tensor upsample_nearest1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_backward::call(grad_output, output_size, input_size, scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::upsample_nearest1d_backward::call(grad_output, output_size, input_size, scales);
  }
}

// aten::_upsample_nearest_exact1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor
inline at::Tensor _upsample_nearest_exact1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales);
  }
}

// aten::_upsample_nearest_exact1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor
inline at::Tensor _upsample_nearest_exact1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_backward::call(grad_output, output_size, input_size, scales);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact1d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact1d_backward::call(grad_output, output_size, input_size, scales);
  }
}

// aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
  }
}

// aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_nearest2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_nearest2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
  }
}

// aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_out::call(self, output_size, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_out::call(self, output_size, scales_h, scales_w, out);
  }
}

// aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_nearest2d_out::call(self, output_size, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_nearest2d_out::call(self, output_size, scales_h, scales_w, out);
  }
}

// aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
  }
}

// aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
  }
}

// aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_out::call(self, output_size, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_out::call(self, output_size, scales_h, scales_w, out);
  }
}

// aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact2d_out::call(self, output_size, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact2d_out::call(self, output_size, scales_h, scales_w, out);
  }
}

// aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w);
  }
}

// aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d::call(self, output_size, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest2d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d::call(self, output_size, scales_h, scales_w);
  }
}

// aten::_upsample_nearest_exact2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_nearest_exact2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w);
  }
}

// aten::_upsample_nearest_exact2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_nearest_exact2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d::call(self, output_size, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact2d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d::call(self, output_size, scales_h, scales_w);
  }
}

// aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
  }
}

// aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
  }
}

// aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact2d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w);
  }
}

// aten::upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_nearest2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w);
  }
}

// aten::_upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_nearest_exact2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w);
  }
}

// aten::_upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_nearest_exact2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w);
  }
}

// aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
  }
}

// aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_nearest3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_nearest3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
  }
}

// aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
  }
}

// aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & upsample_nearest3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_nearest3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::upsample_nearest3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
  }
}

// aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
  }
}

// aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
  }
}

// aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
  }
}

// aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact3d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
    return at::_ops::_upsample_nearest_exact3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
  }
}

// aten::upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w);
  }
}

// aten::upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_nearest3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d::call(self, output_size, scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest3d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d::call(self, output_size, scales_d, scales_h, scales_w);
  }
}

// aten::_upsample_nearest_exact3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_nearest_exact3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w);
  }
}

// aten::_upsample_nearest_exact3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_nearest_exact3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d::call(self, output_size, scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact3d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d::call(self, output_size, scales_d, scales_h, scales_w);
  }
}

// aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & upsample_nearest3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _upsample_nearest_exact3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & _upsample_nearest_exact3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _upsample_nearest_exact3d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
    return at::_ops::_upsample_nearest_exact3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
  }
}

// aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w);
  }
}

// aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor upsample_nearest3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_backward::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::upsample_nearest3d_backward::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w);
  }
}

// aten::_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_nearest_exact3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _upsample_nearest_exact3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w);
  }
}

// aten::_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
inline at::Tensor _upsample_nearest_exact3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_backward::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _upsample_nearest_exact3d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
    return at::_ops::_upsample_nearest_exact3d_backward::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w);
  }
}

// aten::sigmoid_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & sigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) {
    return at::_ops::sigmoid_backward_grad_input::call(grad_output, output, grad_input);
}
// aten::sigmoid_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & sigmoid_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) {
    return at::_ops::sigmoid_backward_grad_input::call(grad_output, output, grad_input);
}

// aten::sigmoid_backward(Tensor grad_output, Tensor output) -> Tensor
inline at::Tensor sigmoid_backward(const at::Tensor & grad_output, const at::Tensor & output) {
    return at::_ops::sigmoid_backward::call(grad_output, output);
}

// aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & logit_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::logit_backward_grad_input::call(grad_output, self, eps, grad_input);
}
// aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & logit_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, ::std::optional<double> eps, at::Tensor & grad_input) {
    return at::_ops::logit_backward_grad_input::call(grad_output, self, eps, grad_input);
}

// aten::logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor
inline at::Tensor logit_backward(const at::Tensor & grad_output, const at::Tensor & self, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::logit_backward::call(grad_output, self, eps);
}

// aten::tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) {
    return at::_ops::tanh_backward_grad_input::call(grad_output, output, grad_input);
}
// aten::tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
inline at::Tensor & tanh_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) {
    return at::_ops::tanh_backward_grad_input::call(grad_output, output, grad_input);
}

// aten::tanh_backward(Tensor grad_output, Tensor output) -> Tensor
inline at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) {
    return at::_ops::tanh_backward::call(grad_output, output);
}

// aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_transpose2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_transpose2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv_transpose2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_transpose2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_transpose2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_transpose2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv_transpose2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_transpose2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_transpose2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_transpose2d_out::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv_transpose2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_transpose2d_out::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out);
  }
}

// aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_transpose2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_transpose2d_out::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv_transpose2d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_transpose2d_out::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out);
  }
}

// aten::slow_conv_transpose2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1) -> Tensor
inline at::Tensor slow_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_transpose2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slow_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_transpose2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::slow_conv_transpose2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1) -> Tensor
inline at::Tensor slow_conv_transpose2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_transpose2d::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slow_conv_transpose2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_transpose2d::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
  }
}

// aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_transpose3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_transpose3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv_transpose3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_transpose3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_transpose3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_transpose3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv_transpose3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_transpose3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_transpose3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_transpose3d_out::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv_transpose3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_transpose3d_out::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out);
  }
}

// aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_transpose3d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_transpose3d_out::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv_transpose3d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_transpose3d_out::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out);
  }
}

// aten::slow_conv_transpose3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1) -> Tensor
inline at::Tensor slow_conv_transpose3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_transpose3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slow_conv_transpose3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_transpose3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::slow_conv_transpose3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1) -> Tensor
inline at::Tensor slow_conv_transpose3d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_transpose3d::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slow_conv_transpose3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_transpose3d::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation);
  }
}

// aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & thnn_conv2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
    return at::_ops::thnn_conv2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & thnn_conv2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
    return at::_ops::thnn_conv2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & thnn_conv2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::thnn_conv2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & thnn_conv2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::thnn_conv2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & thnn_conv2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
    return at::_ops::thnn_conv2d_out::call(self, weight, kernel_size, bias, stride, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & thnn_conv2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
    return at::_ops::thnn_conv2d_out::call(self, weight, kernel_size, bias, stride, padding, out);
  }
}

// aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & thnn_conv2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::thnn_conv2d_out::call(self, weight, kernel_size, bias, stride, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & thnn_conv2d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::thnn_conv2d_out::call(self, weight, kernel_size, bias, stride, padding, out);
  }
}

// aten::thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor
inline at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
    return at::_ops::thnn_conv2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
    return at::_ops::thnn_conv2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
  }
}

// aten::thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor
inline at::Tensor thnn_conv2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
    return at::_ops::thnn_conv2d::call(self, weight, kernel_size, bias, stride, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
    return at::_ops::thnn_conv2d::call(self, weight, kernel_size, bias, stride, padding);
  }
}

// aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!)
inline at::Tensor & _slow_conv2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::_slow_conv2d_forward_output::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _slow_conv2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::_slow_conv2d_forward_output::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output);
  }
}

// aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!)
inline at::Tensor & _slow_conv2d_forward_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & output) {
    return at::_ops::_slow_conv2d_forward_output::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _slow_conv2d_forward_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & output) {
    return at::_ops::_slow_conv2d_forward_output::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output);
  }
}

// aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!)
inline at::Tensor & _slow_conv2d_forward_symint_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::_slow_conv2d_forward_output::call(self, weight, kernel_size, bias, stride, padding, output);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _slow_conv2d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::_slow_conv2d_forward_output::call(self, weight, kernel_size, bias, stride, padding, output);
  }
}

// aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!)
inline at::Tensor & _slow_conv2d_forward_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output) {
    return at::_ops::_slow_conv2d_forward_output::call(self, weight, kernel_size, bias, stride, padding, output);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _slow_conv2d_forward_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output) {
    return at::_ops::_slow_conv2d_forward_output::call(self, weight, kernel_size, bias, stride, padding, output);
  }
}

// aten::_slow_conv2d_forward(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding) -> Tensor
inline at::Tensor _slow_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::_slow_conv2d_forward::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _slow_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::_slow_conv2d_forward::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
  }
}

// aten::_slow_conv2d_forward(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding) -> Tensor
inline at::Tensor _slow_conv2d_forward_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::_slow_conv2d_forward::call(self, weight, kernel_size, bias, stride, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _slow_conv2d_forward(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::_slow_conv2d_forward::call(self, weight, kernel_size, bias, stride, padding);
  }
}

// aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::_slow_conv2d_backward_grad_input::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), grad_input, grad_weight, grad_bias);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::_slow_conv2d_backward_grad_input::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), grad_input, grad_weight, grad_bias);
  }
}

// aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) {
    return at::_ops::_slow_conv2d_backward_grad_input::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), grad_input, grad_weight, grad_bias);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) {
    return at::_ops::_slow_conv2d_backward_grad_input::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), grad_input, grad_weight, grad_bias);
  }
}

// aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_symint_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::_slow_conv2d_backward_grad_input::call(grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::_slow_conv2d_backward_grad_input::call(grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias);
  }
}

// aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) {
    return at::_ops::_slow_conv2d_backward_grad_input::call(grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) {
    return at::_ops::_slow_conv2d_backward_grad_input::call(grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias);
  }
}

// aten::_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _slow_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask) {
    return at::_ops::_slow_conv2d_backward_output_mask::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _slow_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask) {
    return at::_ops::_slow_conv2d_backward_output_mask::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask);
  }
}

// aten::_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _slow_conv2d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array<bool,3> output_mask) {
    return at::_ops::_slow_conv2d_backward_output_mask::call(grad_output, self, weight, kernel_size, stride, padding, output_mask);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _slow_conv2d_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array<bool,3> output_mask) {
    return at::_ops::_slow_conv2d_backward_output_mask::call(grad_output, self, weight, kernel_size, stride, padding, output_mask);
  }
}

// aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & _conv_depthwise2d_out(const at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) {
    return at::_ops::_conv_depthwise2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & _conv_depthwise2d_out(const at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) {
    return at::_ops::_conv_depthwise2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & _conv_depthwise2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, const at::Tensor & out) {
    return at::_ops::_conv_depthwise2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & _conv_depthwise2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, const at::Tensor & out) {
    return at::_ops::_conv_depthwise2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & _conv_depthwise2d_symint_out(const at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) {
    return at::_ops::_conv_depthwise2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & _conv_depthwise2d_out(const at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) {
    return at::_ops::_conv_depthwise2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
  }
}

// aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & _conv_depthwise2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, const at::Tensor & out) {
    return at::_ops::_conv_depthwise2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & _conv_depthwise2d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, const at::Tensor & out) {
    return at::_ops::_conv_depthwise2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
  }
}

// aten::_conv_depthwise2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation) -> Tensor
inline at::Tensor _conv_depthwise2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) {
    return at::_ops::_conv_depthwise2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _conv_depthwise2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) {
    return at::_ops::_conv_depthwise2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::_conv_depthwise2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation) -> Tensor
inline at::Tensor _conv_depthwise2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) {
    return at::_ops::_conv_depthwise2d::call(self, weight, kernel_size, bias, stride, padding, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _conv_depthwise2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) {
    return at::_ops::_conv_depthwise2d::call(self, weight, kernel_size, bias, stride, padding, dilation);
  }
}

// aten::conv_depthwise3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation) -> Tensor
inline at::Tensor conv_depthwise3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) {
    return at::_ops::conv_depthwise3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor conv_depthwise3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) {
    return at::_ops::conv_depthwise3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::conv_depthwise3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation) -> Tensor
inline at::Tensor conv_depthwise3d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) {
    return at::_ops::conv_depthwise3d::call(self, weight, kernel_size, bias, stride, padding, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor conv_depthwise3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) {
    return at::_ops::conv_depthwise3d::call(self, weight, kernel_size, bias, stride, padding, dilation);
  }
}

// aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
    return at::_ops::slow_conv3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
    return at::_ops::slow_conv3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::slow_conv3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
    return at::_ops::slow_conv3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
  }
}

// aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
    return at::_ops::slow_conv3d_out::call(self, weight, kernel_size, bias, stride, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
    return at::_ops::slow_conv3d_out::call(self, weight, kernel_size, bias, stride, padding, out);
  }
}

// aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv3d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::slow_conv3d_out::call(self, weight, kernel_size, bias, stride, padding, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv3d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out) {
    return at::_ops::slow_conv3d_out::call(self, weight, kernel_size, bias, stride, padding, out);
  }
}

// aten::slow_conv3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0) -> Tensor
inline at::Tensor slow_conv3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
    return at::_ops::slow_conv3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slow_conv3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
    return at::_ops::slow_conv3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
  }
}

// aten::slow_conv3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0) -> Tensor
inline at::Tensor slow_conv3d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
    return at::_ops::slow_conv3d::call(self, weight, kernel_size, bias, stride, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slow_conv3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
    return at::_ops::slow_conv3d::call(self, weight, kernel_size, bias, stride, padding);
  }
}

// aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!)
inline at::Tensor & slow_conv3d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::slow_conv3d_forward_output::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv3d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::slow_conv3d_forward_output::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output);
  }
}

// aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!)
inline at::Tensor & slow_conv3d_forward_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & output) {
    return at::_ops::slow_conv3d_forward_output::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv3d_forward_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & output) {
    return at::_ops::slow_conv3d_forward_output::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output);
  }
}

// aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!)
inline at::Tensor & slow_conv3d_forward_symint_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::slow_conv3d_forward_output::call(self, weight, kernel_size, bias, stride, padding, output);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv3d_forward_out(at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::slow_conv3d_forward_output::call(self, weight, kernel_size, bias, stride, padding, output);
  }
}

// aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!)
inline at::Tensor & slow_conv3d_forward_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output) {
    return at::_ops::slow_conv3d_forward_output::call(self, weight, kernel_size, bias, stride, padding, output);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv3d_forward_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output) {
    return at::_ops::slow_conv3d_forward_output::call(self, weight, kernel_size, bias, stride, padding, output);
  }
}

// aten::slow_conv3d_forward(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding) -> Tensor
inline at::Tensor slow_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::slow_conv3d_forward::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slow_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding) {
    return at::_ops::slow_conv3d_forward::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
  }
}

// aten::slow_conv3d_forward(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding) -> Tensor
inline at::Tensor slow_conv3d_forward_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::slow_conv3d_forward::call(self, weight, kernel_size, bias, stride, padding);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slow_conv3d_forward(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) {
    return at::_ops::slow_conv3d_forward::call(self, weight, kernel_size, bias, stride, padding);
  }
}

// aten::slow_conv_dilated2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1) -> Tensor
inline at::Tensor slow_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_dilated2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slow_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_dilated2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::slow_conv_dilated2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1) -> Tensor
inline at::Tensor slow_conv_dilated2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_dilated2d::call(self, weight, kernel_size, bias, stride, padding, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slow_conv_dilated2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_dilated2d::call(self, weight, kernel_size, bias, stride, padding, dilation);
  }
}

// aten::slow_conv_dilated3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1) -> Tensor
inline at::Tensor slow_conv_dilated3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_dilated3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slow_conv_dilated3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_dilated3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation));
  }
}

// aten::slow_conv_dilated3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1) -> Tensor
inline at::Tensor slow_conv_dilated3d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_dilated3d::call(self, weight, kernel_size, bias, stride, padding, dilation);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slow_conv_dilated3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_dilated3d::call(self, weight, kernel_size, bias, stride, padding, dilation);
  }
}

// aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & col2im_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::col2im_out::call(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & col2im_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::col2im_out::call(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride, out);
  }
}

// aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & col2im_outf(const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::col2im_out::call(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & col2im_outf(const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::col2im_out::call(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride, out);
  }
}

// aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & col2im_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::col2im_out::call(self, output_size, kernel_size, dilation, padding, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & col2im_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::col2im_out::call(self, output_size, kernel_size, dilation, padding, stride, out);
  }
}

// aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & col2im_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::col2im_out::call(self, output_size, kernel_size, dilation, padding, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & col2im_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::col2im_out::call(self, output_size, kernel_size, dilation, padding, stride, out);
  }
}

// aten::col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor
inline at::Tensor col2im(const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::col2im::call(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor col2im(const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::col2im::call(self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride);
  }
}

// aten::col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor
inline at::Tensor col2im_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::col2im::call(self, output_size, kernel_size, dilation, padding, stride);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor col2im(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::col2im::call(self, output_size, kernel_size, dilation, padding, stride);
  }
}

// aten::column_stack(Tensor[] tensors) -> Tensor
inline at::Tensor column_stack(at::TensorList tensors) {
    return at::_ops::column_stack::call(tensors);
}

// aten::column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & column_stack_out(at::Tensor & out, at::TensorList tensors) {
    return at::_ops::column_stack_out::call(tensors, out);
}
// aten::column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & column_stack_outf(at::TensorList tensors, at::Tensor & out) {
    return at::_ops::column_stack_out::call(tensors, out);
}

// aten::im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & im2col_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::im2col_out::call(self, kernel_size, dilation, padding, stride, out);
}
// aten::im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & im2col_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::im2col_out::call(self, kernel_size, dilation, padding, stride, out);
}

// aten::im2col(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor
inline at::Tensor im2col(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) {
    return at::_ops::im2col::call(self, kernel_size, dilation, padding, stride);
}

// aten::isfinite(Tensor self) -> Tensor
inline at::Tensor isfinite(const at::Tensor & self) {
    return at::_ops::isfinite::call(self);
}

// aten::isinf(Tensor self) -> Tensor
inline at::Tensor isinf(const at::Tensor & self) {
    return at::_ops::isinf::call(self);
}

// aten::isposinf(Tensor self) -> Tensor
inline at::Tensor isposinf(const at::Tensor & self) {
    return at::_ops::isposinf::call(self);
}

// aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isposinf_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::isposinf_out::call(self, out);
}
// aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isposinf_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::isposinf_out::call(self, out);
}

// aten::isneginf(Tensor self) -> Tensor
inline at::Tensor isneginf(const at::Tensor & self) {
    return at::_ops::isneginf::call(self);
}

// aten::isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isneginf_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::isneginf_out::call(self, out);
}
// aten::isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isneginf_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::isneginf_out::call(self, out);
}

// aten::_add_batch_dim(Tensor self, int batch_dim, int level) -> Tensor
inline at::Tensor _add_batch_dim(const at::Tensor & self, int64_t batch_dim, int64_t level) {
    return at::_ops::_add_batch_dim::call(self, batch_dim, level);
}

// aten::_remove_batch_dim(Tensor self, int level, SymInt batch_size, int out_dim) -> Tensor
inline at::Tensor _remove_batch_dim(const at::Tensor & self, int64_t level, int64_t batch_size, int64_t out_dim) {
    return at::_ops::_remove_batch_dim::call(self, level, batch_size, out_dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _remove_batch_dim(const at::Tensor & self, int64_t level, int64_t batch_size, int64_t out_dim) {
    return at::_ops::_remove_batch_dim::call(self, level, batch_size, out_dim);
  }
}

// aten::_remove_batch_dim(Tensor self, int level, SymInt batch_size, int out_dim) -> Tensor
inline at::Tensor _remove_batch_dim_symint(const at::Tensor & self, int64_t level, c10::SymInt batch_size, int64_t out_dim) {
    return at::_ops::_remove_batch_dim::call(self, level, batch_size, out_dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _remove_batch_dim(const at::Tensor & self, int64_t level, c10::SymInt batch_size, int64_t out_dim) {
    return at::_ops::_remove_batch_dim::call(self, level, batch_size, out_dim);
  }
}

// aten::special_entr(Tensor self) -> Tensor
inline at::Tensor special_entr(const at::Tensor & self) {
    return at::_ops::special_entr::call(self);
}

// aten::special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_entr_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_entr_out::call(self, out);
}
// aten::special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_entr_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_entr_out::call(self, out);
}

// aten::special_ndtri(Tensor self) -> Tensor
inline at::Tensor special_ndtri(const at::Tensor & self) {
    return at::_ops::special_ndtri::call(self);
}

// aten::special_ndtri.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_ndtri_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_ndtri_out::call(self, out);
}
// aten::special_ndtri.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_ndtri_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_ndtri_out::call(self, out);
}

// aten::special_log_ndtr(Tensor self) -> Tensor
inline at::Tensor special_log_ndtr(const at::Tensor & self) {
    return at::_ops::special_log_ndtr::call(self);
}

// aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_log_ndtr_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_log_ndtr_out::call(self, out);
}
// aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_log_ndtr_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_log_ndtr_out::call(self, out);
}

// aten::special_expm1(Tensor self) -> Tensor
inline at::Tensor special_expm1(const at::Tensor & self) {
    return at::_ops::special_expm1::call(self);
}

// aten::special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_expm1_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_expm1_out::call(self, out);
}
// aten::special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_expm1_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_expm1_out::call(self, out);
}

// aten::special_exp2(Tensor self) -> Tensor
inline at::Tensor special_exp2(const at::Tensor & self) {
    return at::_ops::special_exp2::call(self);
}

// aten::special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_exp2_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_exp2_out::call(self, out);
}
// aten::special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_exp2_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_exp2_out::call(self, out);
}

// aten::special_psi(Tensor self) -> Tensor
inline at::Tensor special_psi(const at::Tensor & self) {
    return at::_ops::special_psi::call(self);
}

// aten::special_psi.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_psi_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_psi_out::call(self, out);
}
// aten::special_psi.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_psi_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_psi_out::call(self, out);
}

// aten::special_digamma(Tensor self) -> Tensor
inline at::Tensor special_digamma(const at::Tensor & self) {
    return at::_ops::special_digamma::call(self);
}

// aten::special_digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_digamma_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_digamma_out::call(self, out);
}
// aten::special_digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_digamma_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_digamma_out::call(self, out);
}

// aten::special_gammaln(Tensor self) -> Tensor
inline at::Tensor special_gammaln(const at::Tensor & self) {
    return at::_ops::special_gammaln::call(self);
}

// aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_gammaln_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_gammaln_out::call(self, out);
}
// aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_gammaln_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_gammaln_out::call(self, out);
}

// aten::special_erf(Tensor self) -> Tensor
inline at::Tensor special_erf(const at::Tensor & self) {
    return at::_ops::special_erf::call(self);
}

// aten::special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_erf_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_erf_out::call(self, out);
}
// aten::special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_erf_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_erf_out::call(self, out);
}

// aten::special_erfc(Tensor self) -> Tensor
inline at::Tensor special_erfc(const at::Tensor & self) {
    return at::_ops::special_erfc::call(self);
}

// aten::special_erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_erfc_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_erfc_out::call(self, out);
}
// aten::special_erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_erfc_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_erfc_out::call(self, out);
}

// aten::special_erfcx(Tensor self) -> Tensor
inline at::Tensor special_erfcx(const at::Tensor & self) {
    return at::_ops::special_erfcx::call(self);
}

// aten::special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_erfcx_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_erfcx_out::call(self, out);
}
// aten::special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_erfcx_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_erfcx_out::call(self, out);
}

// aten::special_erfinv(Tensor self) -> Tensor
inline at::Tensor special_erfinv(const at::Tensor & self) {
    return at::_ops::special_erfinv::call(self);
}

// aten::special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_erfinv_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_erfinv_out::call(self, out);
}
// aten::special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_erfinv_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_erfinv_out::call(self, out);
}

// aten::special_ndtr(Tensor self) -> Tensor
inline at::Tensor special_ndtr(const at::Tensor & self) {
    return at::_ops::special_ndtr::call(self);
}

// aten::special_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_ndtr_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_ndtr_out::call(self, out);
}
// aten::special_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_ndtr_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_ndtr_out::call(self, out);
}

// aten::special_xlog1py(Tensor self, Tensor other) -> Tensor
inline at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_xlog1py::call(self, other);
}

// aten::special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor
inline at::Tensor special_xlog1py(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::special_xlog1py_self_scalar::call(self, other);
}

// aten::special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor special_xlog1py(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::special_xlog1py_other_scalar::call(self, other);
}

// aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_xlog1py_out::call(self, other, out);
}
// aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::special_xlog1py_out::call(self, other, out);
}

// aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::special_xlog1py_self_scalar_out::call(self, other, out);
}
// aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlog1py_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::special_xlog1py_self_scalar_out::call(self, other, out);
}

// aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::special_xlog1py_other_scalar_out::call(self, other, out);
}
// aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::special_xlog1py_other_scalar_out::call(self, other, out);
}

// aten::special_xlogy(Tensor self, Tensor other) -> Tensor
inline at::Tensor special_xlogy(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_xlogy::call(self, other);
}

// aten::special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor
inline at::Tensor special_xlogy(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::special_xlogy_self_scalar::call(self, other);
}

// aten::special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor special_xlogy(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::special_xlogy_other_scalar::call(self, other);
}

// aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_xlogy_out::call(self, other, out);
}
// aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::special_xlogy_out::call(self, other, out);
}

// aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::special_xlogy_self_scalar_out::call(self, other, out);
}
// aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::special_xlogy_self_scalar_out::call(self, other, out);
}

// aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::special_xlogy_other_scalar_out::call(self, other, out);
}
// aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::special_xlogy_other_scalar_out::call(self, other, out);
}

// aten::special_zeta(Tensor self, Tensor other) -> Tensor
inline at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_zeta::call(self, other);
}

// aten::special_zeta.self_scalar(Scalar self, Tensor other) -> Tensor
inline at::Tensor special_zeta(const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::special_zeta_self_scalar::call(self, other);
}

// aten::special_zeta.other_scalar(Tensor self, Scalar other) -> Tensor
inline at::Tensor special_zeta(const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::special_zeta_other_scalar::call(self, other);
}

// aten::special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_zeta_out::call(self, other, out);
}
// aten::special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::special_zeta_out::call(self, other, out);
}

// aten::special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_zeta_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::special_zeta_self_scalar_out::call(self, other, out);
}
// aten::special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_zeta_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::special_zeta_self_scalar_out::call(self, other, out);
}

// aten::special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::special_zeta_other_scalar_out::call(self, other, out);
}
// aten::special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::special_zeta_other_scalar_out::call(self, other, out);
}

// aten::special_i0(Tensor self) -> Tensor
inline at::Tensor special_i0(const at::Tensor & self) {
    return at::_ops::special_i0::call(self);
}

// aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_i0_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_i0_out::call(self, out);
}
// aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_i0_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_i0_out::call(self, out);
}

// aten::special_i0e(Tensor self) -> Tensor
inline at::Tensor special_i0e(const at::Tensor & self) {
    return at::_ops::special_i0e::call(self);
}

// aten::special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_i0e_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_i0e_out::call(self, out);
}
// aten::special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_i0e_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_i0e_out::call(self, out);
}

// aten::special_i1(Tensor self) -> Tensor
inline at::Tensor special_i1(const at::Tensor & self) {
    return at::_ops::special_i1::call(self);
}

// aten::special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_i1_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_i1_out::call(self, out);
}
// aten::special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_i1_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_i1_out::call(self, out);
}

// aten::special_i1e(Tensor self) -> Tensor
inline at::Tensor special_i1e(const at::Tensor & self) {
    return at::_ops::special_i1e::call(self);
}

// aten::special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_i1e_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_i1e_out::call(self, out);
}
// aten::special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_i1e_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_i1e_out::call(self, out);
}

// aten::special_logit(Tensor self, float? eps=None) -> Tensor
inline at::Tensor special_logit(const at::Tensor & self, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::special_logit::call(self, eps);
}

// aten::special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_logit_out(at::Tensor & out, const at::Tensor & self, ::std::optional<double> eps=::std::nullopt) {
    return at::_ops::special_logit_out::call(self, eps, out);
}
// aten::special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_logit_outf(const at::Tensor & self, ::std::optional<double> eps, at::Tensor & out) {
    return at::_ops::special_logit_out::call(self, eps, out);
}

// aten::special_polygamma(int n, Tensor self) -> Tensor
inline at::Tensor special_polygamma(int64_t n, const at::Tensor & self) {
    return at::_ops::special_polygamma::call(n, self);
}

// aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_polygamma_out(at::Tensor & out, int64_t n, const at::Tensor & self) {
    return at::_ops::special_polygamma_out::call(n, self, out);
}
// aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_polygamma_outf(int64_t n, const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_polygamma_out::call(n, self, out);
}

// aten::special_logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor
inline at::Tensor special_logsumexp(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::special_logsumexp::call(self, dim, keepdim);
}

// aten::special_logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_logsumexp_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) {
    return at::_ops::special_logsumexp_out::call(self, dim, keepdim, out);
}
// aten::special_logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_logsumexp_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) {
    return at::_ops::special_logsumexp_out::call(self, dim, keepdim, out);
}

// aten::special_expit(Tensor self) -> Tensor
inline at::Tensor special_expit(const at::Tensor & self) {
    return at::_ops::special_expit::call(self);
}

// aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_expit_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_expit_out::call(self, out);
}
// aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_expit_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_expit_out::call(self, out);
}

// aten::special_sinc(Tensor self) -> Tensor
inline at::Tensor special_sinc(const at::Tensor & self) {
    return at::_ops::special_sinc::call(self);
}

// aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_sinc_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_sinc_out::call(self, out);
}
// aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_sinc_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_sinc_out::call(self, out);
}

// aten::special_round(Tensor self, *, int decimals=0) -> Tensor
inline at::Tensor special_round(const at::Tensor & self, int64_t decimals=0) {
    return at::_ops::special_round::call(self, decimals);
}

// aten::special_round.out(Tensor self, *, int decimals=0, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_round_out(at::Tensor & out, const at::Tensor & self, int64_t decimals=0) {
    return at::_ops::special_round_out::call(self, decimals, out);
}
// aten::special_round.out(Tensor self, *, int decimals=0, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_round_outf(const at::Tensor & self, int64_t decimals, at::Tensor & out) {
    return at::_ops::special_round_out::call(self, decimals, out);
}

// aten::special_log1p(Tensor self) -> Tensor
inline at::Tensor special_log1p(const at::Tensor & self) {
    return at::_ops::special_log1p::call(self);
}

// aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_log1p_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_log1p_out::call(self, out);
}
// aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_log1p_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_log1p_out::call(self, out);
}

// aten::special_log_softmax(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor special_log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::special_log_softmax::call(self, dim, dtype);
}

// aten::special_gammainc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_gammainc_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_gammainc_out::call(self, other, out);
}
// aten::special_gammainc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_gammainc_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::special_gammainc_out::call(self, other, out);
}

// aten::special_gammainc(Tensor self, Tensor other) -> Tensor
inline at::Tensor special_gammainc(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_gammainc::call(self, other);
}

// aten::special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_gammaincc_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_gammaincc_out::call(self, other, out);
}
// aten::special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_gammaincc_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::special_gammaincc_out::call(self, other, out);
}

// aten::special_gammaincc(Tensor self, Tensor other) -> Tensor
inline at::Tensor special_gammaincc(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::special_gammaincc::call(self, other);
}

// aten::special_multigammaln(Tensor self, int p) -> Tensor
inline at::Tensor special_multigammaln(const at::Tensor & self, int64_t p) {
    return at::_ops::special_multigammaln::call(self, p);
}

// aten::special_multigammaln.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_multigammaln_out(at::Tensor & out, const at::Tensor & self, int64_t p) {
    return at::_ops::special_multigammaln_out::call(self, p, out);
}
// aten::special_multigammaln.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_multigammaln_outf(const at::Tensor & self, int64_t p, at::Tensor & out) {
    return at::_ops::special_multigammaln_out::call(self, p, out);
}

// aten::special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
inline at::Tensor special_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::special_softmax::call(self, dim, dtype);
}

// aten::fft_fft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_fft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_fft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_fft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_fft_symint(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft::call(self, n, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_fft(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft::call(self, n, dim, norm);
  }
}

// aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_fft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_fft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fft_symint_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_fft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fft_symint_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_fft_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_ifft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_ifft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_ifft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_ifft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_ifft_symint(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft::call(self, n, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_ifft(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft::call(self, n, dim, norm);
  }
}

// aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_ifft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_ifft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifft_symint_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_ifft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifft_symint_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_ifft_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_rfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_rfft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_rfft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_rfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_rfft_symint(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft::call(self, n, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_rfft(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft::call(self, n, dim, norm);
  }
}

// aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_rfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_rfft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfft_symint_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_rfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfft_symint_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_rfft_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_irfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_irfft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_irfft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_irfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_irfft_symint(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft::call(self, n, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_irfft(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft::call(self, n, dim, norm);
  }
}

// aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_irfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_irfft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfft_symint_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_irfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfft_symint_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_irfft_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_hfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_hfft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_hfft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_hfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_hfft_symint(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft::call(self, n, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_hfft(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft::call(self, n, dim, norm);
  }
}

// aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_hfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_hfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_hfft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_hfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_hfft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_hfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_hfft_symint_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_hfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_hfft_symint_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_hfft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_hfft_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_hfft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_ihfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_ihfft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_ihfft(const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_ihfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor
inline at::Tensor fft_ihfft_symint(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft::call(self, n, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_ihfft(const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft::call(self, n, dim, norm);
  }
}

// aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ihfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_ihfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ihfft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ihfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_ihfft_outf(const at::Tensor & self, ::std::optional<int64_t> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ihfft_out::call(self, n.has_value() ? ::std::make_optional(c10::SymInt(*n)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ihfft_symint_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_ihfft_out(at::Tensor & out, const at::Tensor & self, ::std::optional<c10::SymInt> n=::std::nullopt, int64_t dim=-1, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ihfft_symint_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ihfft_out::call(self, n, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_ihfft_outf(const at::Tensor & self, ::std::optional<c10::SymInt> n, int64_t dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ihfft_out::call(self, n, dim, norm, out);
  }
}

// aten::fft_fft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_fft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_fft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_fft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_fft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft2::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_fft2(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft2::call(self, s, dim, norm);
  }
}

// aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_fft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_fft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fft2_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_fft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fft2_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_fft2_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_ifft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_ifft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_ifft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_ifft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_ifft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft2::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_ifft2(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft2::call(self, s, dim, norm);
  }
}

// aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_ifft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_ifft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifft2_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_ifft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifft2_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_ifft2_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_rfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_rfft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_rfft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_rfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_rfft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft2::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_rfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft2::call(self, s, dim, norm);
  }
}

// aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_rfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_rfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfft2_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_rfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfft2_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_rfft2_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_irfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_irfft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_irfft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_irfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_irfft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft2::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_irfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft2::call(self, s, dim, norm);
  }
}

// aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_irfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_irfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfft2_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_irfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfft2_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_irfft2_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_hfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_hfft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_hfft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_hfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_hfft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft2::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_hfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft2::call(self, s, dim, norm);
  }
}

// aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_hfft2_out(const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & fft_hfft2_out(const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_hfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_hfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & fft_hfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_hfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_hfft2_symint_out(const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & fft_hfft2_out(const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_hfft2_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_hfft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & fft_hfft2_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_hfft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_ihfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_ihfft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_ihfft2(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft2::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_ihfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor
inline at::Tensor fft_ihfft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft2::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_ihfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft2::call(self, s, dim, norm);
  }
}

// aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_ihfft2_out(const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & fft_ihfft2_out(const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_ihfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_ihfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & fft_ihfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_ihfft2_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_ihfft2_symint_out(const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & fft_ihfft2_out(const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_ihfft2_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_ihfft2_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & fft_ihfft2_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_ihfft2_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_fftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_fftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_fftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_fftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_fftn_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fftn::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_fftn(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fftn::call(self, s, dim, norm);
  }
}

// aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_fftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_fftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fftn_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_fftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_fftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fftn_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_fftn_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_fftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_ifftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_ifftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_ifftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_ifftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_ifftn_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifftn::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_ifftn(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifftn::call(self, s, dim, norm);
  }
}

// aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_ifftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_ifftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifftn_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_ifftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ifftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_ifftn_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_ifftn_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_ifftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_rfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_rfftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_rfftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_rfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_rfftn_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfftn::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_rfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfftn::call(self, s, dim, norm);
  }
}

// aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_rfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_rfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfftn_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_rfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_rfftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfftn_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_rfftn_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_rfftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_irfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_irfftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_irfftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_irfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_irfftn_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfftn::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_irfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfftn::call(self, s, dim, norm);
  }
}

// aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_irfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & fft_irfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfftn_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_irfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_irfftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_irfftn_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & fft_irfftn_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, at::Tensor & out) {
    return at::_ops::fft_irfftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_hfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_hfftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_hfftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_hfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_hfftn_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfftn::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_hfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfftn::call(self, s, dim, norm);
  }
}

// aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_hfftn_out(const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & fft_hfftn_out(const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_hfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_hfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & fft_hfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_hfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_hfftn_symint_out(const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & fft_hfftn_out(const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_hfftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_hfftn_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_hfftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & fft_hfftn_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_hfftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_ihfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_ihfftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor fft_ihfftn(const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfftn::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm);
  }
}

// aten::fft_ihfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
inline at::Tensor fft_ihfftn_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfftn::call(self, s, dim, norm);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor fft_ihfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfftn::call(self, s, dim, norm);
  }
}

// aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_ihfftn_out(const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & fft_ihfftn_out(const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_ihfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_ihfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & fft_ihfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_ihfftn_out::call(self, s.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*s)) : ::std::nullopt, dim, norm, out);
  }
}

// aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_ihfftn_symint_out(const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & fft_ihfftn_out(const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, ::std::optional<c10::string_view> norm=::std::nullopt) {
    return at::_ops::fft_ihfftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & fft_ihfftn_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_ihfftn_out::call(self, s, dim, norm, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & fft_ihfftn_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out) {
    return at::_ops::fft_ihfftn_out::call(self, s, dim, norm, out);
  }
}

// aten::fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor fft_fftfreq(int64_t n, double d=1.0, at::TensorOptions options={}) {
    return at::_ops::fft_fftfreq::call(n, d, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor fft_fftfreq(int64_t n, double d, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::fft_fftfreq::call(n, d, dtype, layout, device, pin_memory);
}

// aten::fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fftfreq_out(at::Tensor & out, int64_t n, double d=1.0) {
    return at::_ops::fft_fftfreq_out::call(n, d, out);
}
// aten::fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_fftfreq_outf(int64_t n, double d, at::Tensor & out) {
    return at::_ops::fft_fftfreq_out::call(n, d, out);
}

// aten::fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor fft_rfftfreq(int64_t n, double d=1.0, at::TensorOptions options={}) {
    return at::_ops::fft_rfftfreq::call(n, d, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
}
// aten::fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor fft_rfftfreq(int64_t n, double d, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
    return at::_ops::fft_rfftfreq::call(n, d, dtype, layout, device, pin_memory);
}

// aten::fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfftfreq_out(at::Tensor & out, int64_t n, double d=1.0) {
    return at::_ops::fft_rfftfreq_out::call(n, d, out);
}
// aten::fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fft_rfftfreq_outf(int64_t n, double d, at::Tensor & out) {
    return at::_ops::fft_rfftfreq_out::call(n, d, out);
}

// aten::fft_fftshift(Tensor self, int[1]? dim=None) -> Tensor
inline at::Tensor fft_fftshift(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt) {
    return at::_ops::fft_fftshift::call(self, dim);
}

// aten::fft_ifftshift(Tensor self, int[1]? dim=None) -> Tensor
inline at::Tensor fft_ifftshift(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt) {
    return at::_ops::fft_ifftshift::call(self, dim);
}

// aten::linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_cholesky_ex(const at::Tensor & self, bool upper=false, bool check_errors=false) {
    return at::_ops::linalg_cholesky_ex::call(self, upper, check_errors);
}

// aten::linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_cholesky_ex_out(at::Tensor & L, at::Tensor & info, const at::Tensor & self, bool upper=false, bool check_errors=false) {
    return at::_ops::linalg_cholesky_ex_L::call(self, upper, check_errors, L, info);
}
// aten::linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_cholesky_ex_outf(const at::Tensor & self, bool upper, bool check_errors, at::Tensor & L, at::Tensor & info) {
    return at::_ops::linalg_cholesky_ex_L::call(self, upper, check_errors, L, info);
}

// aten::linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor
inline at::Tensor linalg_cholesky(const at::Tensor & self, bool upper=false) {
    return at::_ops::linalg_cholesky::call(self, upper);
}

// aten::linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_cholesky_out(at::Tensor & out, const at::Tensor & self, bool upper=false) {
    return at::_ops::linalg_cholesky_out::call(self, upper, out);
}
// aten::linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_cholesky_outf(const at::Tensor & self, bool upper, at::Tensor & out) {
    return at::_ops::linalg_cholesky_out::call(self, upper, out);
}

// aten::linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor
inline at::Tensor linalg_cross(const at::Tensor & self, const at::Tensor & other, int64_t dim=-1) {
    return at::_ops::linalg_cross::call(self, other, dim);
}

// aten::linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_cross_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, int64_t dim=-1) {
    return at::_ops::linalg_cross_out::call(self, other, dim, out);
}
// aten::linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_cross_outf(const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out) {
    return at::_ops::linalg_cross_out::call(self, other, dim, out);
}

// aten::linalg_lu_factor(Tensor A, *, bool pivot=True) -> (Tensor LU, Tensor pivots)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_lu_factor(const at::Tensor & A, bool pivot=true) {
    return at::_ops::linalg_lu_factor::call(A, pivot);
}

// aten::linalg_lu_factor.out(Tensor A, *, bool pivot=True, Tensor(a!) LU, Tensor(b!) pivots) -> (Tensor(a!) LU, Tensor(b!) pivots)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_lu_factor_out(at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A, bool pivot=true) {
    return at::_ops::linalg_lu_factor_out::call(A, pivot, LU, pivots);
}
// aten::linalg_lu_factor.out(Tensor A, *, bool pivot=True, Tensor(a!) LU, Tensor(b!) pivots) -> (Tensor(a!) LU, Tensor(b!) pivots)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_lu_factor_outf(const at::Tensor & A, bool pivot, at::Tensor & LU, at::Tensor & pivots) {
    return at::_ops::linalg_lu_factor_out::call(A, pivot, LU, pivots);
}

// aten::linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu_factor_ex(const at::Tensor & A, bool pivot=true, bool check_errors=false) {
    return at::_ops::linalg_lu_factor_ex::call(A, pivot, check_errors);
}

// aten::linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_out(at::Tensor & LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & A, bool pivot=true, bool check_errors=false) {
    return at::_ops::linalg_lu_factor_ex_out::call(A, pivot, check_errors, LU, pivots, info);
}
// aten::linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_factor_ex_outf(const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) {
    return at::_ops::linalg_lu_factor_ex_out::call(A, pivot, check_errors, LU, pivots, info);
}

// aten::linalg_lu(Tensor A, *, bool pivot=True) -> (Tensor P, Tensor L, Tensor U)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_lu(const at::Tensor & A, bool pivot=true) {
    return at::_ops::linalg_lu::call(A, pivot);
}

// aten::linalg_lu.out(Tensor A, *, bool pivot=True, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_out(at::Tensor & P, at::Tensor & L, at::Tensor & U, const at::Tensor & A, bool pivot=true) {
    return at::_ops::linalg_lu_out::call(A, pivot, P, L, U);
}
// aten::linalg_lu.out(Tensor A, *, bool pivot=True, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_lu_outf(const at::Tensor & A, bool pivot, at::Tensor & P, at::Tensor & L, at::Tensor & U) {
    return at::_ops::linalg_lu_out::call(A, pivot, P, L, U);
}

// aten::linalg_lu_solve(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False) -> Tensor
inline at::Tensor linalg_lu_solve(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left=true, bool adjoint=false) {
    return at::_ops::linalg_lu_solve::call(LU, pivots, B, left, adjoint);
}

// aten::linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_lu_solve_out(at::Tensor & out, const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left=true, bool adjoint=false) {
    return at::_ops::linalg_lu_solve_out::call(LU, pivots, B, left, adjoint, out);
}
// aten::linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_lu_solve_outf(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint, at::Tensor & out) {
    return at::_ops::linalg_lu_solve_out::call(LU, pivots, B, left, adjoint, out);
}

// aten::_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_det(const at::Tensor & A) {
    return at::_ops::_linalg_det::call(A);
}

// aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_det_out(at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A) {
    return at::_ops::_linalg_det_result::call(A, result, LU, pivots);
}
// aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_det_outf(const at::Tensor & A, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots) {
    return at::_ops::_linalg_det_result::call(A, result, LU, pivots);
}

// aten::linalg_det(Tensor A) -> Tensor
inline at::Tensor linalg_det(const at::Tensor & A) {
    return at::_ops::linalg_det::call(A);
}

// aten::linalg_det.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_det_out(at::Tensor & out, const at::Tensor & A) {
    return at::_ops::linalg_det_out::call(A, out);
}
// aten::linalg_det.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_det_outf(const at::Tensor & A, at::Tensor & out) {
    return at::_ops::linalg_det_out::call(A, out);
}

// aten::det(Tensor self) -> Tensor
inline at::Tensor det(const at::Tensor & self) {
    return at::_ops::det::call(self);
}

// aten::linalg_ldl_factor_ex(Tensor self, *, bool hermitian=False, bool check_errors=False) -> (Tensor LD, Tensor pivots, Tensor info)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_ldl_factor_ex(const at::Tensor & self, bool hermitian=false, bool check_errors=false) {
    return at::_ops::linalg_ldl_factor_ex::call(self, hermitian, check_errors);
}

// aten::linalg_ldl_factor_ex.out(Tensor self, *, bool hermitian=False, bool check_errors=False, Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_ldl_factor_ex_out(at::Tensor & LD, at::Tensor & pivots, at::Tensor & info, const at::Tensor & self, bool hermitian=false, bool check_errors=false) {
    return at::_ops::linalg_ldl_factor_ex_out::call(self, hermitian, check_errors, LD, pivots, info);
}
// aten::linalg_ldl_factor_ex.out(Tensor self, *, bool hermitian=False, bool check_errors=False, Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_ldl_factor_ex_outf(const at::Tensor & self, bool hermitian, bool check_errors, at::Tensor & LD, at::Tensor & pivots, at::Tensor & info) {
    return at::_ops::linalg_ldl_factor_ex_out::call(self, hermitian, check_errors, LD, pivots, info);
}

// aten::linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_ldl_factor(const at::Tensor & self, bool hermitian=false) {
    return at::_ops::linalg_ldl_factor::call(self, hermitian);
}

// aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_ldl_factor_out(at::Tensor & LD, at::Tensor & pivots, const at::Tensor & self, bool hermitian=false) {
    return at::_ops::linalg_ldl_factor_out::call(self, hermitian, LD, pivots);
}
// aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_ldl_factor_outf(const at::Tensor & self, bool hermitian, at::Tensor & LD, at::Tensor & pivots) {
    return at::_ops::linalg_ldl_factor_out::call(self, hermitian, LD, pivots);
}

// aten::linalg_ldl_solve(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False) -> Tensor
inline at::Tensor linalg_ldl_solve(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian=false) {
    return at::_ops::linalg_ldl_solve::call(LD, pivots, B, hermitian);
}

// aten::linalg_ldl_solve.out(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_ldl_solve_out(at::Tensor & out, const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian=false) {
    return at::_ops::linalg_ldl_solve_out::call(LD, pivots, B, hermitian, out);
}
// aten::linalg_ldl_solve.out(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_ldl_solve_outf(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_ldl_solve_out::call(LD, pivots, B, hermitian, out);
}

// aten::linalg_lstsq(Tensor self, Tensor b, float? rcond=None, *, str? driver=None) -> (Tensor solution, Tensor residuals, Tensor rank, Tensor singular_values)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> linalg_lstsq(const at::Tensor & self, const at::Tensor & b, ::std::optional<double> rcond=::std::nullopt, ::std::optional<c10::string_view> driver=::std::nullopt) {
    return at::_ops::linalg_lstsq::call(self, b, rcond, driver);
}

// aten::linalg_lstsq.out(Tensor self, Tensor b, float? rcond=None, *, str? driver=None, Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) -> (Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> linalg_lstsq_out(at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values, const at::Tensor & self, const at::Tensor & b, ::std::optional<double> rcond=::std::nullopt, ::std::optional<c10::string_view> driver=::std::nullopt) {
    return at::_ops::linalg_lstsq_out::call(self, b, rcond, driver, solution, residuals, rank, singular_values);
}
// aten::linalg_lstsq.out(Tensor self, Tensor b, float? rcond=None, *, str? driver=None, Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) -> (Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> linalg_lstsq_outf(const at::Tensor & self, const at::Tensor & b, ::std::optional<double> rcond, ::std::optional<c10::string_view> driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values) {
    return at::_ops::linalg_lstsq_out::call(self, b, rcond, driver, solution, residuals, rank, singular_values);
}

// aten::linalg_matmul(Tensor self, Tensor other) -> Tensor
inline at::Tensor linalg_matmul(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::linalg_matmul::call(self, other);
}

// aten::linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matmul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::linalg_matmul_out::call(self, other, out);
}
// aten::linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matmul_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::linalg_matmul_out::call(self, other, out);
}

// aten::linalg_vecdot(Tensor x, Tensor y, *, int dim=-1) -> Tensor
inline at::Tensor linalg_vecdot(const at::Tensor & x, const at::Tensor & y, int64_t dim=-1) {
    return at::_ops::linalg_vecdot::call(x, y, dim);
}

// aten::linalg_vecdot.out(Tensor x, Tensor y, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_vecdot_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & y, int64_t dim=-1) {
    return at::_ops::linalg_vecdot_out::call(x, y, dim, out);
}
// aten::linalg_vecdot.out(Tensor x, Tensor y, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_vecdot_outf(const at::Tensor & x, const at::Tensor & y, int64_t dim, at::Tensor & out) {
    return at::_ops::linalg_vecdot_out::call(x, y, dim, out);
}

// aten::linalg_matrix_exp(Tensor self) -> Tensor
inline at::Tensor linalg_matrix_exp(const at::Tensor & self) {
    return at::_ops::linalg_matrix_exp::call(self);
}

// aten::_linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet, Tensor LU, Tensor pivots)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _linalg_slogdet(const at::Tensor & A) {
    return at::_ops::_linalg_slogdet::call(A);
}

// aten::_linalg_slogdet.sign(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) -> (Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_slogdet_out(at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A) {
    return at::_ops::_linalg_slogdet_sign::call(A, sign, logabsdet, LU, pivots);
}
// aten::_linalg_slogdet.sign(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) -> (Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_slogdet_outf(const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots) {
    return at::_ops::_linalg_slogdet_sign::call(A, sign, logabsdet, LU, pivots);
}

// aten::linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_slogdet(const at::Tensor & A) {
    return at::_ops::linalg_slogdet::call(A);
}

// aten::linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_slogdet_out(at::Tensor & sign, at::Tensor & logabsdet, const at::Tensor & A) {
    return at::_ops::linalg_slogdet_out::call(A, sign, logabsdet);
}
// aten::linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_slogdet_outf(const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet) {
    return at::_ops::linalg_slogdet_out::call(A, sign, logabsdet);
}

// aten::slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet)
inline ::std::tuple<at::Tensor,at::Tensor> slogdet(const at::Tensor & self) {
    return at::_ops::slogdet::call(self);
}

// aten::slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)
inline ::std::tuple<at::Tensor &,at::Tensor &> slogdet_out(at::Tensor & sign, at::Tensor & logabsdet, const at::Tensor & self) {
    return at::_ops::slogdet_out::call(self, sign, logabsdet);
}
// aten::slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet)
inline ::std::tuple<at::Tensor &,at::Tensor &> slogdet_outf(const at::Tensor & self, at::Tensor & sign, at::Tensor & logabsdet) {
    return at::_ops::slogdet_out::call(self, sign, logabsdet);
}

// aten::logdet(Tensor self) -> Tensor
inline at::Tensor logdet(const at::Tensor & self) {
    return at::_ops::logdet::call(self);
}

// aten::linalg_eig(Tensor self) -> (Tensor eigenvalues, Tensor eigenvectors)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_eig(const at::Tensor & self) {
    return at::_ops::linalg_eig::call(self);
}

// aten::linalg_eig.out(Tensor self, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_eig_out(at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & self) {
    return at::_ops::linalg_eig_out::call(self, eigenvalues, eigenvectors);
}
// aten::linalg_eig.out(Tensor self, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_eig_outf(const at::Tensor & self, at::Tensor & eigenvalues, at::Tensor & eigenvectors) {
    return at::_ops::linalg_eig_out::call(self, eigenvalues, eigenvectors);
}

// aten::_linalg_eigvals(Tensor self) -> Tensor
inline at::Tensor _linalg_eigvals(const at::Tensor & self) {
    return at::_ops::_linalg_eigvals::call(self);
}

// aten::linalg_eigvals(Tensor self) -> Tensor
inline at::Tensor linalg_eigvals(const at::Tensor & self) {
    return at::_ops::linalg_eigvals::call(self);
}

// aten::linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_eigvals_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::linalg_eigvals_out::call(self, out);
}
// aten::linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_eigvals_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::linalg_eigvals_out::call(self, out);
}

// aten::_linalg_eigh(Tensor A, str UPLO="L", bool compute_v=True) -> (Tensor eigenvalues, Tensor eigenvectors)
inline ::std::tuple<at::Tensor,at::Tensor> _linalg_eigh(const at::Tensor & A, c10::string_view UPLO="L", bool compute_v=true) {
    return at::_ops::_linalg_eigh::call(A, UPLO, compute_v);
}

// aten::_linalg_eigh.eigenvalues(Tensor A, str UPLO="L", bool compute_v=True, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
inline ::std::tuple<at::Tensor &,at::Tensor &> _linalg_eigh_out(at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & A, c10::string_view UPLO="L", bool compute_v=true) {
    return at::_ops::_linalg_eigh_eigenvalues::call(A, UPLO, compute_v, eigenvalues, eigenvectors);
}
// aten::_linalg_eigh.eigenvalues(Tensor A, str UPLO="L", bool compute_v=True, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
inline ::std::tuple<at::Tensor &,at::Tensor &> _linalg_eigh_outf(const at::Tensor & A, c10::string_view UPLO, bool compute_v, at::Tensor & eigenvalues, at::Tensor & eigenvectors) {
    return at::_ops::_linalg_eigh_eigenvalues::call(A, UPLO, compute_v, eigenvalues, eigenvectors);
}

// aten::linalg_eigh(Tensor self, str UPLO="L") -> (Tensor eigenvalues, Tensor eigenvectors)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_eigh(const at::Tensor & self, c10::string_view UPLO="L") {
    return at::_ops::linalg_eigh::call(self, UPLO);
}

// aten::linalg_eigh.eigvals(Tensor self, str UPLO="L", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_eigh_out(at::Tensor & eigvals, at::Tensor & eigvecs, const at::Tensor & self, c10::string_view UPLO="L") {
    return at::_ops::linalg_eigh_eigvals::call(self, UPLO, eigvals, eigvecs);
}
// aten::linalg_eigh.eigvals(Tensor self, str UPLO="L", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_eigh_outf(const at::Tensor & self, c10::string_view UPLO, at::Tensor & eigvals, at::Tensor & eigvecs) {
    return at::_ops::linalg_eigh_eigvals::call(self, UPLO, eigvals, eigvecs);
}

// aten::linalg_eigvalsh(Tensor self, str UPLO="L") -> Tensor
inline at::Tensor linalg_eigvalsh(const at::Tensor & self, c10::string_view UPLO="L") {
    return at::_ops::linalg_eigvalsh::call(self, UPLO);
}

// aten::linalg_eigvalsh.out(Tensor self, str UPLO="L", *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_eigvalsh_out(at::Tensor & out, const at::Tensor & self, c10::string_view UPLO="L") {
    return at::_ops::linalg_eigvalsh_out::call(self, UPLO, out);
}
// aten::linalg_eigvalsh.out(Tensor self, str UPLO="L", *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_eigvalsh_outf(const at::Tensor & self, c10::string_view UPLO, at::Tensor & out) {
    return at::_ops::linalg_eigvalsh_out::call(self, UPLO, out);
}

// aten::linalg_householder_product(Tensor input, Tensor tau) -> Tensor
inline at::Tensor linalg_householder_product(const at::Tensor & input, const at::Tensor & tau) {
    return at::_ops::linalg_householder_product::call(input, tau);
}

// aten::linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_householder_product_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & tau) {
    return at::_ops::linalg_householder_product_out::call(input, tau, out);
}
// aten::linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_householder_product_outf(const at::Tensor & input, const at::Tensor & tau, at::Tensor & out) {
    return at::_ops::linalg_householder_product_out::call(input, tau, out);
}

// aten::linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_inv_ex(const at::Tensor & A, bool check_errors=false) {
    return at::_ops::linalg_inv_ex::call(A, check_errors);
}

// aten::linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_inv_ex_out(at::Tensor & inverse, at::Tensor & info, const at::Tensor & A, bool check_errors=false) {
    return at::_ops::linalg_inv_ex_inverse::call(A, check_errors, inverse, info);
}
// aten::linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_inv_ex_outf(const at::Tensor & A, bool check_errors, at::Tensor & inverse, at::Tensor & info) {
    return at::_ops::linalg_inv_ex_inverse::call(A, check_errors, inverse, info);
}

// aten::linalg_inv(Tensor A) -> Tensor
inline at::Tensor linalg_inv(const at::Tensor & A) {
    return at::_ops::linalg_inv::call(A);
}

// aten::linalg_inv.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_inv_out(at::Tensor & out, const at::Tensor & A) {
    return at::_ops::linalg_inv_out::call(A, out);
}
// aten::linalg_inv.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_inv_outf(const at::Tensor & A, at::Tensor & out) {
    return at::_ops::linalg_inv_out::call(A, out);
}

// aten::inverse(Tensor self) -> Tensor
inline at::Tensor inverse(const at::Tensor & self) {
    return at::_ops::inverse::call(self);
}

// aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & inverse_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::inverse_out::call(self, out);
}
// aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & inverse_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::inverse_out::call(self, out);
}

// aten::inner(Tensor self, Tensor other) -> Tensor
inline at::Tensor inner(const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::inner::call(self, other);
}

// aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & inner_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::inner_out::call(self, other, out);
}
// aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & inner_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::inner_out::call(self, other, out);
}

// aten::outer(Tensor self, Tensor vec2) -> Tensor
inline at::Tensor outer(const at::Tensor & self, const at::Tensor & vec2) {
    return at::_ops::outer::call(self, vec2);
}

// aten::outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & outer_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & vec2) {
    return at::_ops::outer_out::call(self, vec2, out);
}
// aten::outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & outer_outf(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out) {
    return at::_ops::outer_out::call(self, vec2, out);
}

// aten::ger(Tensor self, Tensor vec2) -> Tensor
inline at::Tensor ger(const at::Tensor & self, const at::Tensor & vec2) {
    return at::_ops::ger::call(self, vec2);
}

// aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ger_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & vec2) {
    return at::_ops::ger_out::call(self, vec2, out);
}
// aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ger_outf(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out) {
    return at::_ops::ger_out::call(self, vec2, out);
}

// aten::linalg_norm(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor linalg_norm(const at::Tensor & self, const ::std::optional<at::Scalar> & ord=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_norm::call(self, ord, dim, keepdim, dtype);
}

// aten::linalg_norm.ord_str(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor linalg_norm(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_norm_ord_str::call(self, ord, dim, keepdim, dtype);
}

// aten::linalg_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_norm_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & ord=::std::nullopt, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_norm_out::call(self, ord, dim, keepdim, dtype, out);
}
// aten::linalg_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_norm_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::linalg_norm_out::call(self, ord, dim, keepdim, dtype, out);
}

// aten::linalg_norm.ord_str_out(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_norm_out(at::Tensor & out, const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_norm_ord_str_out::call(self, ord, dim, keepdim, dtype, out);
}
// aten::linalg_norm.ord_str_out(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_norm_outf(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::linalg_norm_ord_str_out::call(self, ord, dim, keepdim, dtype, out);
}

// aten::linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor linalg_vector_norm(const at::Tensor & self, const at::Scalar & ord=2, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_vector_norm::call(self, ord, dim, keepdim, dtype);
}

// aten::linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_vector_norm_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & ord=2, at::OptionalIntArrayRef dim=::std::nullopt, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_vector_norm_out::call(self, ord, dim, keepdim, dtype, out);
}
// aten::linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_vector_norm_outf(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::linalg_vector_norm_out::call(self, ord, dim, keepdim, dtype, out);
}

// aten::linalg_matrix_norm(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor linalg_matrix_norm(const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim={-2,-1}, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_matrix_norm::call(self, ord, dim, keepdim, dtype);
}

// aten::linalg_matrix_norm.out(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_norm_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim={-2,-1}, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_matrix_norm_out::call(self, ord, dim, keepdim, dtype, out);
}
// aten::linalg_matrix_norm.out(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_norm_outf(const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::linalg_matrix_norm_out::call(self, ord, dim, keepdim, dtype, out);
}

// aten::linalg_matrix_norm.str_ord(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
inline at::Tensor linalg_matrix_norm(const at::Tensor & self, c10::string_view ord="fro", at::IntArrayRef dim={-2,-1}, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_matrix_norm_str_ord::call(self, ord, dim, keepdim, dtype);
}

// aten::linalg_matrix_norm.str_ord_out(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_norm_out(at::Tensor & out, const at::Tensor & self, c10::string_view ord="fro", at::IntArrayRef dim={-2,-1}, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::linalg_matrix_norm_str_ord_out::call(self, ord, dim, keepdim, dtype, out);
}
// aten::linalg_matrix_norm.str_ord_out(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_norm_outf(const at::Tensor & self, c10::string_view ord, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::linalg_matrix_norm_str_ord_out::call(self, ord, dim, keepdim, dtype, out);
}

// aten::_linalg_svd(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _linalg_svd(const at::Tensor & A, bool full_matrices=false, bool compute_uv=true, ::std::optional<c10::string_view> driver=::std::nullopt) {
    return at::_ops::_linalg_svd::call(A, full_matrices, compute_uv, driver);
}

// aten::_linalg_svd.U(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & Vh, const at::Tensor & A, bool full_matrices=false, bool compute_uv=true, ::std::optional<c10::string_view> driver=::std::nullopt) {
    return at::_ops::_linalg_svd_U::call(A, full_matrices, compute_uv, driver, U, S, Vh);
}
// aten::_linalg_svd.U(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _linalg_svd_outf(const at::Tensor & A, bool full_matrices, bool compute_uv, ::std::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh) {
    return at::_ops::_linalg_svd_U::call(A, full_matrices, compute_uv, driver, U, S, Vh);
}

// aten::linalg_svd(Tensor A, bool full_matrices=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> linalg_svd(const at::Tensor & A, bool full_matrices=true, ::std::optional<c10::string_view> driver=::std::nullopt) {
    return at::_ops::linalg_svd::call(A, full_matrices, driver);
}

// aten::linalg_svd.U(Tensor A, bool full_matrices=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & Vh, const at::Tensor & A, bool full_matrices=true, ::std::optional<c10::string_view> driver=::std::nullopt) {
    return at::_ops::linalg_svd_U::call(A, full_matrices, driver, U, S, Vh);
}
// aten::linalg_svd.U(Tensor A, bool full_matrices=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linalg_svd_outf(const at::Tensor & A, bool full_matrices, ::std::optional<c10::string_view> driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh) {
    return at::_ops::linalg_svd_U::call(A, full_matrices, driver, U, S, Vh);
}

// aten::linalg_svdvals(Tensor A, *, str? driver=None) -> Tensor
inline at::Tensor linalg_svdvals(const at::Tensor & A, ::std::optional<c10::string_view> driver=::std::nullopt) {
    return at::_ops::linalg_svdvals::call(A, driver);
}

// aten::linalg_svdvals.out(Tensor A, *, str? driver=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_svdvals_out(at::Tensor & out, const at::Tensor & A, ::std::optional<c10::string_view> driver=::std::nullopt) {
    return at::_ops::linalg_svdvals_out::call(A, driver, out);
}
// aten::linalg_svdvals.out(Tensor A, *, str? driver=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_svdvals_outf(const at::Tensor & A, ::std::optional<c10::string_view> driver, at::Tensor & out) {
    return at::_ops::linalg_svdvals_out::call(A, driver, out);
}

// aten::linalg_cond(Tensor self, Scalar? p=None) -> Tensor
inline at::Tensor linalg_cond(const at::Tensor & self, const ::std::optional<at::Scalar> & p=::std::nullopt) {
    return at::_ops::linalg_cond::call(self, p);
}

// aten::linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_cond_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & p=::std::nullopt) {
    return at::_ops::linalg_cond_out::call(self, p, out);
}
// aten::linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_cond_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::Tensor & out) {
    return at::_ops::linalg_cond_out::call(self, p, out);
}

// aten::linalg_cond.p_str(Tensor self, str p) -> Tensor
inline at::Tensor linalg_cond(const at::Tensor & self, c10::string_view p) {
    return at::_ops::linalg_cond_p_str::call(self, p);
}

// aten::linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_cond_out(at::Tensor & out, const at::Tensor & self, c10::string_view p) {
    return at::_ops::linalg_cond_p_str_out::call(self, p, out);
}
// aten::linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_cond_outf(const at::Tensor & self, c10::string_view p, at::Tensor & out) {
    return at::_ops::linalg_cond_p_str_out::call(self, p, out);
}

// aten::linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor
inline at::Tensor linalg_pinv(const at::Tensor & self, const ::std::optional<at::Tensor> & atol={}, const ::std::optional<at::Tensor> & rtol={}, bool hermitian=false) {
    return at::_ops::linalg_pinv_atol_rtol_tensor::call(self, atol, rtol, hermitian);
}

// aten::linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_pinv_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Tensor> & atol={}, const ::std::optional<at::Tensor> & rtol={}, bool hermitian=false) {
    return at::_ops::linalg_pinv_atol_rtol_tensor_out::call(self, atol, rtol, hermitian, out);
}
// aten::linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_pinv_outf(const at::Tensor & self, const ::std::optional<at::Tensor> & atol, const ::std::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_pinv_atol_rtol_tensor_out::call(self, atol, rtol, hermitian, out);
}

// aten::linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor
inline at::Tensor linalg_pinv(const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian=false) {
    return at::_ops::linalg_pinv_atol_rtol_float::call(self, atol, rtol, hermitian);
}

// aten::linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_pinv_out(at::Tensor & out, const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian=false) {
    return at::_ops::linalg_pinv_atol_rtol_float_out::call(self, atol, rtol, hermitian, out);
}
// aten::linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_pinv_outf(const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_pinv_atol_rtol_float_out::call(self, atol, rtol, hermitian, out);
}

// aten::linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor
inline at::Tensor linalg_pinv(const at::Tensor & self, double rcond, bool hermitian=false) {
    return at::_ops::linalg_pinv::call(self, rcond, hermitian);
}

// aten::linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor
inline at::Tensor linalg_pinv(const at::Tensor & self, const at::Tensor & rcond, bool hermitian=false) {
    return at::_ops::linalg_pinv_rcond_tensor::call(self, rcond, hermitian);
}

// aten::linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_pinv_out(at::Tensor & out, const at::Tensor & self, double rcond, bool hermitian=false) {
    return at::_ops::linalg_pinv_out::call(self, rcond, hermitian, out);
}
// aten::linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_pinv_outf(const at::Tensor & self, double rcond, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_pinv_out::call(self, rcond, hermitian, out);
}

// aten::linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_pinv_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & rcond, bool hermitian=false) {
    return at::_ops::linalg_pinv_out_rcond_tensor::call(self, rcond, hermitian, out);
}
// aten::linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_pinv_outf(const at::Tensor & self, const at::Tensor & rcond, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_pinv_out_rcond_tensor::call(self, rcond, hermitian, out);
}

// aten::_linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor LU, Tensor pivots, Tensor info)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _linalg_solve_ex(const at::Tensor & A, const at::Tensor & B, bool left=true, bool check_errors=false) {
    return at::_ops::_linalg_solve_ex::call(A, B, left, check_errors);
}

// aten::_linalg_solve_ex.result(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_solve_ex_out(at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & A, const at::Tensor & B, bool left=true, bool check_errors=false) {
    return at::_ops::_linalg_solve_ex_result::call(A, B, left, check_errors, result, LU, pivots, info);
}
// aten::_linalg_solve_ex.result(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _linalg_solve_ex_outf(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) {
    return at::_ops::_linalg_solve_ex_result::call(A, B, left, check_errors, result, LU, pivots, info);
}

// aten::linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_solve_ex(const at::Tensor & A, const at::Tensor & B, bool left=true, bool check_errors=false) {
    return at::_ops::linalg_solve_ex::call(A, B, left, check_errors);
}

// aten::linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_solve_ex_out(at::Tensor & result, at::Tensor & info, const at::Tensor & A, const at::Tensor & B, bool left=true, bool check_errors=false) {
    return at::_ops::linalg_solve_ex_out::call(A, B, left, check_errors, result, info);
}
// aten::linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_solve_ex_outf(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & info) {
    return at::_ops::linalg_solve_ex_out::call(A, B, left, check_errors, result, info);
}

// aten::linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor
inline at::Tensor linalg_solve(const at::Tensor & A, const at::Tensor & B, bool left=true) {
    return at::_ops::linalg_solve::call(A, B, left);
}

// aten::_spsolve(Tensor A, Tensor B, *, bool left=True) -> Tensor
inline at::Tensor _spsolve(const at::Tensor & A, const at::Tensor & B, bool left=true) {
    return at::_ops::_spsolve::call(A, B, left);
}

// aten::linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_solve_out(at::Tensor & out, const at::Tensor & A, const at::Tensor & B, bool left=true) {
    return at::_ops::linalg_solve_out::call(A, B, left, out);
}
// aten::linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_solve_outf(const at::Tensor & A, const at::Tensor & B, bool left, at::Tensor & out) {
    return at::_ops::linalg_solve_out::call(A, B, left, out);
}

// aten::linalg_tensorinv(Tensor self, int ind=2) -> Tensor
inline at::Tensor linalg_tensorinv(const at::Tensor & self, int64_t ind=2) {
    return at::_ops::linalg_tensorinv::call(self, ind);
}

// aten::linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_tensorinv_out(at::Tensor & out, const at::Tensor & self, int64_t ind=2) {
    return at::_ops::linalg_tensorinv_out::call(self, ind, out);
}
// aten::linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_tensorinv_outf(const at::Tensor & self, int64_t ind, at::Tensor & out) {
    return at::_ops::linalg_tensorinv_out::call(self, ind, out);
}

// aten::linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor
inline at::Tensor linalg_tensorsolve(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims=::std::nullopt) {
    return at::_ops::linalg_tensorsolve::call(self, other, dims);
}

// aten::linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_tensorsolve_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims=::std::nullopt) {
    return at::_ops::linalg_tensorsolve_out::call(self, other, dims, out);
}
// aten::linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_tensorsolve_outf(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims, at::Tensor & out) {
    return at::_ops::linalg_tensorsolve_out::call(self, other, dims, out);
}

// aten::linalg_qr(Tensor A, str mode='reduced') -> (Tensor Q, Tensor R)
inline ::std::tuple<at::Tensor,at::Tensor> linalg_qr(const at::Tensor & A, c10::string_view mode="reduced") {
    return at::_ops::linalg_qr::call(A, mode);
}

// aten::linalg_qr.out(Tensor A, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_qr_out(at::Tensor & Q, at::Tensor & R, const at::Tensor & A, c10::string_view mode="reduced") {
    return at::_ops::linalg_qr_out::call(A, mode, Q, R);
}
// aten::linalg_qr.out(Tensor A, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)
inline ::std::tuple<at::Tensor &,at::Tensor &> linalg_qr_outf(const at::Tensor & A, c10::string_view mode, at::Tensor & Q, at::Tensor & R) {
    return at::_ops::linalg_qr_out::call(A, mode, Q, R);
}

// aten::linalg_matrix_power(Tensor self, int n) -> Tensor
inline at::Tensor linalg_matrix_power(const at::Tensor & self, int64_t n) {
    return at::_ops::linalg_matrix_power::call(self, n);
}

// aten::linalg_matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_power_out(at::Tensor & out, const at::Tensor & self, int64_t n) {
    return at::_ops::linalg_matrix_power_out::call(self, n, out);
}
// aten::linalg_matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_power_outf(const at::Tensor & self, int64_t n, at::Tensor & out) {
    return at::_ops::linalg_matrix_power_out::call(self, n, out);
}

// aten::linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor
inline at::Tensor linalg_matrix_rank(const at::Tensor & input, const ::std::optional<at::Tensor> & atol={}, const ::std::optional<at::Tensor> & rtol={}, bool hermitian=false) {
    return at::_ops::linalg_matrix_rank_atol_rtol_tensor::call(input, atol, rtol, hermitian);
}

// aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_rank_out(at::Tensor & out, const at::Tensor & input, const ::std::optional<at::Tensor> & atol={}, const ::std::optional<at::Tensor> & rtol={}, bool hermitian=false) {
    return at::_ops::linalg_matrix_rank_atol_rtol_tensor_out::call(input, atol, rtol, hermitian, out);
}
// aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_rank_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & atol, const ::std::optional<at::Tensor> & rtol, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_matrix_rank_atol_rtol_tensor_out::call(input, atol, rtol, hermitian, out);
}

// aten::linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor
inline at::Tensor linalg_matrix_rank(const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian=false) {
    return at::_ops::linalg_matrix_rank_atol_rtol_float::call(self, atol, rtol, hermitian);
}

// aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_rank_out(at::Tensor & out, const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian=false) {
    return at::_ops::linalg_matrix_rank_atol_rtol_float_out::call(self, atol, rtol, hermitian, out);
}
// aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_rank_outf(const at::Tensor & self, ::std::optional<double> atol, ::std::optional<double> rtol, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_matrix_rank_atol_rtol_float_out::call(self, atol, rtol, hermitian, out);
}

// aten::linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor
inline at::Tensor linalg_matrix_rank(const at::Tensor & self, double tol, bool hermitian=false) {
    return at::_ops::linalg_matrix_rank::call(self, tol, hermitian);
}

// aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_rank_out(at::Tensor & out, const at::Tensor & self, double tol, bool hermitian=false) {
    return at::_ops::linalg_matrix_rank_out::call(self, tol, hermitian, out);
}
// aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_rank_outf(const at::Tensor & self, double tol, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_matrix_rank_out::call(self, tol, hermitian, out);
}

// aten::linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor
inline at::Tensor linalg_matrix_rank(const at::Tensor & input, const at::Tensor & tol, bool hermitian=false) {
    return at::_ops::linalg_matrix_rank_tol_tensor::call(input, tol, hermitian);
}

// aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_rank_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & tol, bool hermitian=false) {
    return at::_ops::linalg_matrix_rank_out_tol_tensor::call(input, tol, hermitian, out);
}
// aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_rank_outf(const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out) {
    return at::_ops::linalg_matrix_rank_out_tol_tensor::call(input, tol, hermitian, out);
}

// aten::linalg_multi_dot(Tensor[] tensors) -> Tensor
inline at::Tensor linalg_multi_dot(at::TensorList tensors) {
    return at::_ops::linalg_multi_dot::call(tensors);
}

// aten::linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_multi_dot_out(at::Tensor & out, at::TensorList tensors) {
    return at::_ops::linalg_multi_dot_out::call(tensors, out);
}
// aten::linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_multi_dot_outf(at::TensorList tensors, at::Tensor & out) {
    return at::_ops::linalg_multi_dot_out::call(tensors, out);
}

// aten::nested_to_padded_tensor(Tensor self, float padding, int[]? output_size=None) -> Tensor
inline at::Tensor nested_to_padded_tensor(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt) {
    return at::_ops::nested_to_padded_tensor::call(self, padding, output_size);
}

// aten::_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor
inline at::Tensor _test_serialization_subcmul(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::_test_serialization_subcmul::call(self, other, alpha);
}

// aten::_test_parallel_materialize(Tensor self, int num_parallel, bool skip_first=False) -> Tensor
inline at::Tensor _test_parallel_materialize(const at::Tensor & self, int64_t num_parallel, bool skip_first=false) {
    return at::_ops::_test_parallel_materialize::call(self, num_parallel, skip_first);
}

// aten::_test_optional_intlist(Tensor values, int[]? addends) -> Tensor
inline at::Tensor _test_optional_intlist(const at::Tensor & values, at::OptionalIntArrayRef addends) {
    return at::_ops::_test_optional_intlist::call(values, addends);
}

// aten::_test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor
inline at::Tensor _test_optional_filled_intlist(const at::Tensor & values, at::OptionalIntArrayRef addends) {
    return at::_ops::_test_optional_filled_intlist::call(values, addends);
}

// aten::_test_optional_floatlist(Tensor values, float[]? addends) -> Tensor
inline at::Tensor _test_optional_floatlist(const at::Tensor & values, ::std::optional<at::ArrayRef<double>> addends) {
    return at::_ops::_test_optional_floatlist::call(values, addends);
}

// aten::_test_string_default(Tensor dummy, str a="\"'\\", str b='"\'\\') -> Tensor
inline at::Tensor _test_string_default(const at::Tensor & dummy, c10::string_view a="\"'\\", c10::string_view b="\"'\\") {
    return at::_ops::_test_string_default::call(dummy, a, b);
}

// aten::_test_ambiguous_defaults.a(Tensor dummy, int a=1, int b=1) -> Tensor
inline at::Tensor _test_ambiguous_defaults(const at::Tensor & dummy, int64_t a=1, int64_t b=1) {
    return at::_ops::_test_ambiguous_defaults_a::call(dummy, a, b);
}

// aten::_test_ambiguous_defaults.b(Tensor dummy, int a=2, str b="2") -> Tensor
inline at::Tensor _test_ambiguous_defaults(const at::Tensor & dummy, int64_t a, c10::string_view b) {
    return at::_ops::_test_ambiguous_defaults_b::call(dummy, a, b);
}

// aten::_test_warn_in_autograd(Tensor self) -> Tensor
inline at::Tensor _test_warn_in_autograd(const at::Tensor & self) {
    return at::_ops::_test_warn_in_autograd::call(self);
}

// aten::_test_autograd_multiple_dispatch.fullcoverage(Tensor self) -> Tensor
inline at::Tensor _test_autograd_multiple_dispatch(const at::Tensor & self) {
    return at::_ops::_test_autograd_multiple_dispatch_fullcoverage::call(self);
}

// aten::_test_autograd_multiple_dispatch.ntonly(Tensor self, bool b) -> Tensor
inline at::Tensor _test_autograd_multiple_dispatch(const at::Tensor & self, bool b) {
    return at::_ops::_test_autograd_multiple_dispatch_ntonly::call(self, b);
}

// aten::_test_autograd_multiple_dispatch_view(Tensor(a) self) -> Tensor(a)
inline at::Tensor _test_autograd_multiple_dispatch_view(const at::Tensor & self) {
    return at::_ops::_test_autograd_multiple_dispatch_view::call(self);
}

// aten::_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor
inline at::Tensor _test_autograd_multiple_dispatch_view_copy(const at::Tensor & self) {
    return at::_ops::_test_autograd_multiple_dispatch_view_copy::call(self);
}

// aten::segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None) -> Tensor
inline at::Tensor segment_reduce(const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths={}, const ::std::optional<at::Tensor> & indices={}, const ::std::optional<at::Tensor> & offsets={}, int64_t axis=0, bool unsafe=false, const ::std::optional<at::Scalar> & initial=::std::nullopt) {
    return at::_ops::segment_reduce::call(data, reduce, lengths, indices, offsets, axis, unsafe, initial);
}

// aten::_segment_reduce_backward(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None) -> Tensor
inline at::Tensor _segment_reduce_backward(const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths={}, const ::std::optional<at::Tensor> & offsets={}, int64_t axis=0, const ::std::optional<at::Scalar> & initial=::std::nullopt) {
    return at::_ops::_segment_reduce_backward::call(grad, output, data, reduce, lengths, offsets, axis, initial);
}

// aten::pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0, str padding_side="right") -> Tensor
inline at::Tensor pad_sequence(at::TensorList sequences, bool batch_first=false, double padding_value=0.0, c10::string_view padding_side="right") {
    return at::_ops::pad_sequence::call(sequences, batch_first, padding_value, padding_side);
}

// aten::flatten_dense_tensors(Tensor[] tensors) -> Tensor
inline at::Tensor flatten_dense_tensors(at::TensorList tensors) {
    return at::_ops::flatten_dense_tensors::call(tensors);
}

// aten::unflatten_dense_tensors(Tensor flat, Tensor[] tensors) -> Tensor[]
inline ::std::vector<at::Tensor> unflatten_dense_tensors(const at::Tensor & flat, at::TensorList tensors) {
    return at::_ops::unflatten_dense_tensors::call(flat, tensors);
}

// aten::_nested_tensor_from_tensor_list(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
inline at::Tensor _nested_tensor_from_tensor_list(at::TensorList list, ::std::optional<at::ScalarType> dtype=::std::nullopt, ::std::optional<at::Layout> layout=::std::nullopt, ::std::optional<at::Device> device=::std::nullopt, ::std::optional<bool> pin_memory=::std::nullopt) {
    return at::_ops::_nested_tensor_from_tensor_list::call(list, dtype, layout, device, pin_memory);
}

// aten::_fw_primal_copy(Tensor self, int level) -> Tensor
inline at::Tensor _fw_primal_copy(const at::Tensor & self, int64_t level) {
    return at::_ops::_fw_primal_copy::call(self, level);
}

// aten::_make_dual_copy(Tensor primal, Tensor tangent, int level) -> Tensor
inline at::Tensor _make_dual_copy(const at::Tensor & primal, const at::Tensor & tangent, int64_t level) {
    return at::_ops::_make_dual_copy::call(primal, tangent, level);
}

// aten::view_as_real_copy(Tensor self) -> Tensor
inline at::Tensor view_as_real_copy(const at::Tensor & self) {
    return at::_ops::view_as_real_copy::call(self);
}

// aten::view_as_complex_copy(Tensor self) -> Tensor
inline at::Tensor view_as_complex_copy(const at::Tensor & self) {
    return at::_ops::view_as_complex_copy::call(self);
}

// aten::_conj_copy(Tensor self) -> Tensor
inline at::Tensor _conj_copy(const at::Tensor & self) {
    return at::_ops::_conj_copy::call(self);
}

// aten::_neg_view_copy(Tensor self) -> Tensor
inline at::Tensor _neg_view_copy(const at::Tensor & self) {
    return at::_ops::_neg_view_copy::call(self);
}

// aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor
inline at::Tensor as_strided_copy(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_copy::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor as_strided_copy(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_copy::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt);
  }
}

// aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor
inline at::Tensor as_strided_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_copy::call(self, size, stride, storage_offset);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor as_strided_copy(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_copy::call(self, size, stride, storage_offset);
  }
}

// aten::_sparse_broadcast_to_copy(Tensor self, int[] size) -> Tensor
inline at::Tensor _sparse_broadcast_to_copy(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_sparse_broadcast_to_copy::call(self, size);
}

// aten::diagonal_copy(Tensor self, int offset=0, int dim1=0, int dim2=1) -> Tensor
inline at::Tensor diagonal_copy(const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) {
    return at::_ops::diagonal_copy::call(self, offset, dim1, dim2);
}

// aten::expand_copy(Tensor self, SymInt[] size, *, bool implicit=False) -> Tensor
inline at::Tensor expand_copy(const at::Tensor & self, at::IntArrayRef size, bool implicit=false) {
    return at::_ops::expand_copy::call(self, c10::fromIntArrayRefSlow(size), implicit);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor expand_copy(const at::Tensor & self, at::IntArrayRef size, bool implicit=false) {
    return at::_ops::expand_copy::call(self, c10::fromIntArrayRefSlow(size), implicit);
  }
}

// aten::expand_copy(Tensor self, SymInt[] size, *, bool implicit=False) -> Tensor
inline at::Tensor expand_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit=false) {
    return at::_ops::expand_copy::call(self, size, implicit);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor expand_copy(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit=false) {
    return at::_ops::expand_copy::call(self, size, implicit);
  }
}

// aten::permute_copy(Tensor self, int[] dims) -> Tensor
inline at::Tensor permute_copy(const at::Tensor & self, at::IntArrayRef dims) {
    return at::_ops::permute_copy::call(self, dims);
}

// aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor
inline at::Tensor _reshape_alias_copy(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::_reshape_alias_copy::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _reshape_alias_copy(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::_reshape_alias_copy::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
  }
}

// aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor
inline at::Tensor _reshape_alias_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::_reshape_alias_copy::call(self, size, stride);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _reshape_alias_copy(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::_reshape_alias_copy::call(self, size, stride);
  }
}

// aten::select_copy.int(Tensor self, int dim, SymInt index) -> Tensor
inline at::Tensor select_copy(const at::Tensor & self, int64_t dim, int64_t index) {
    return at::_ops::select_copy_int::call(self, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor select_copy(const at::Tensor & self, int64_t dim, int64_t index) {
    return at::_ops::select_copy_int::call(self, dim, index);
  }
}

// aten::select_copy.int(Tensor self, int dim, SymInt index) -> Tensor
inline at::Tensor select_copy_symint(const at::Tensor & self, int64_t dim, c10::SymInt index) {
    return at::_ops::select_copy_int::call(self, dim, index);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor select_copy(const at::Tensor & self, int64_t dim, c10::SymInt index) {
    return at::_ops::select_copy_int::call(self, dim, index);
  }
}

// aten::detach_copy(Tensor self) -> Tensor
inline at::Tensor detach_copy(const at::Tensor & self) {
    return at::_ops::detach_copy::call(self);
}

// aten::slice_copy.Tensor(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor
inline at::Tensor slice_copy(const at::Tensor & self, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_copy_Tensor::call(self, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor slice_copy(const at::Tensor & self, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_copy_Tensor::call(self, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step);
  }
}

// aten::slice_copy.Tensor(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor
inline at::Tensor slice_copy_symint(const at::Tensor & self, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_copy_Tensor::call(self, dim, start, end, step);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor slice_copy(const at::Tensor & self, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_copy_Tensor::call(self, dim, start, end, step);
  }
}

// aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> split_copy(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::split_copy_Tensor::call(self, split_size, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> split_copy(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::split_copy_Tensor::call(self, split_size, dim);
  }
}

// aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> split_copy_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::split_copy_Tensor::call(self, split_size, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> split_copy(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::split_copy_Tensor::call(self, split_size, dim);
  }
}

// aten::split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> split_with_sizes_copy(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes_copy::call(self, c10::fromIntArrayRefSlow(split_sizes), dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::vector<at::Tensor> split_with_sizes_copy(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes_copy::call(self, c10::fromIntArrayRefSlow(split_sizes), dim);
  }
}

// aten::split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> split_with_sizes_copy_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes_copy::call(self, split_sizes, dim);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::vector<at::Tensor> split_with_sizes_copy(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes_copy::call(self, split_sizes, dim);
  }
}

// aten::squeeze_copy(Tensor self) -> Tensor
inline at::Tensor squeeze_copy(const at::Tensor & self) {
    return at::_ops::squeeze_copy::call(self);
}

// aten::squeeze_copy.dim(Tensor self, int dim) -> Tensor
inline at::Tensor squeeze_copy(const at::Tensor & self, int64_t dim) {
    return at::_ops::squeeze_copy_dim::call(self, dim);
}

// aten::squeeze_copy.dims(Tensor self, int[] dim) -> Tensor
inline at::Tensor squeeze_copy(const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::squeeze_copy_dims::call(self, dim);
}

// aten::t_copy(Tensor self) -> Tensor
inline at::Tensor t_copy(const at::Tensor & self) {
    return at::_ops::t_copy::call(self);
}

// aten::transpose_copy.int(Tensor self, int dim0, int dim1) -> Tensor
inline at::Tensor transpose_copy(const at::Tensor & self, int64_t dim0, int64_t dim1) {
    return at::_ops::transpose_copy_int::call(self, dim0, dim1);
}

// aten::unsqueeze_copy(Tensor self, int dim) -> Tensor
inline at::Tensor unsqueeze_copy(const at::Tensor & self, int64_t dim) {
    return at::_ops::unsqueeze_copy::call(self, dim);
}

// aten::_indices_copy(Tensor self) -> Tensor
inline at::Tensor _indices_copy(const at::Tensor & self) {
    return at::_ops::_indices_copy::call(self);
}

// aten::_values_copy(Tensor self) -> Tensor
inline at::Tensor _values_copy(const at::Tensor & self) {
    return at::_ops::_values_copy::call(self);
}

// aten::indices_copy(Tensor self) -> Tensor
inline at::Tensor indices_copy(const at::Tensor & self) {
    return at::_ops::indices_copy::call(self);
}

// aten::values_copy(Tensor self) -> Tensor
inline at::Tensor values_copy(const at::Tensor & self) {
    return at::_ops::values_copy::call(self);
}

// aten::crow_indices_copy(Tensor self) -> Tensor
inline at::Tensor crow_indices_copy(const at::Tensor & self) {
    return at::_ops::crow_indices_copy::call(self);
}

// aten::col_indices_copy(Tensor self) -> Tensor
inline at::Tensor col_indices_copy(const at::Tensor & self) {
    return at::_ops::col_indices_copy::call(self);
}

// aten::ccol_indices_copy(Tensor self) -> Tensor
inline at::Tensor ccol_indices_copy(const at::Tensor & self) {
    return at::_ops::ccol_indices_copy::call(self);
}

// aten::row_indices_copy(Tensor self) -> Tensor
inline at::Tensor row_indices_copy(const at::Tensor & self) {
    return at::_ops::row_indices_copy::call(self);
}

// aten::unbind_copy.int(Tensor self, int dim=0) -> Tensor[]
inline ::std::vector<at::Tensor> unbind_copy(const at::Tensor & self, int64_t dim=0) {
    return at::_ops::unbind_copy_int::call(self, dim);
}

// aten::unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unbind_copy_out(at::TensorList out, const at::Tensor & self, int64_t dim=0) {
    return at::_ops::unbind_copy_int_out::call(self, dim, out);
}
// aten::unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unbind_copy_outf(const at::Tensor & self, int64_t dim, at::TensorList out) {
    return at::_ops::unbind_copy_int_out::call(self, dim, out);
}

// aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
inline void split_copy_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void split_copy_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
  }
}

// aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
inline void split_copy_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) {
    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void split_copy_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) {
    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
  }
}

// aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
inline void split_copy_symint_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void split_copy_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
  }
}

// aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
inline void split_copy_symint_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) {
    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void split_copy_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) {
    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
  }
}

// aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()
inline void split_with_sizes_copy_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes_copy_out::call(self, c10::fromIntArrayRefSlow(split_sizes), dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void split_with_sizes_copy_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes_copy_out::call(self, c10::fromIntArrayRefSlow(split_sizes), dim, out);
  }
}

// aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()
inline void split_with_sizes_copy_outf(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out) {
    return at::_ops::split_with_sizes_copy_out::call(self, c10::fromIntArrayRefSlow(split_sizes), dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void split_with_sizes_copy_outf(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out) {
    return at::_ops::split_with_sizes_copy_out::call(self, c10::fromIntArrayRefSlow(split_sizes), dim, out);
  }
}

// aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()
inline void split_with_sizes_copy_symint_out(at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes_copy_out::call(self, split_sizes, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void split_with_sizes_copy_out(at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::split_with_sizes_copy_out::call(self, split_sizes, dim, out);
  }
}

// aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()
inline void split_with_sizes_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) {
    return at::_ops::split_with_sizes_copy_out::call(self, split_sizes, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void split_with_sizes_copy_outf(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) {
    return at::_ops::split_with_sizes_copy_out::call(self, split_sizes, dim, out);
  }
}

// aten::view_copy(Tensor self, SymInt[] size) -> Tensor
inline at::Tensor view_copy(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::view_copy::call(self, c10::fromIntArrayRefSlow(size));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor view_copy(const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::view_copy::call(self, c10::fromIntArrayRefSlow(size));
  }
}

// aten::view_copy(Tensor self, SymInt[] size) -> Tensor
inline at::Tensor view_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::view_copy::call(self, size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor view_copy(const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::view_copy::call(self, size);
  }
}

// aten::view_copy.dtype(Tensor self, ScalarType dtype) -> Tensor
inline at::Tensor view_copy(const at::Tensor & self, at::ScalarType dtype) {
    return at::_ops::view_copy_dtype::call(self, dtype);
}

// aten::unfold_copy(Tensor self, int dimension, int size, int step) -> Tensor
inline at::Tensor unfold_copy(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) {
    return at::_ops::unfold_copy::call(self, dimension, size, step);
}

// aten::alias_copy(Tensor self) -> Tensor
inline at::Tensor alias_copy(const at::Tensor & self) {
    return at::_ops::alias_copy::call(self);
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor to_padded_tensor(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt) {
    return at::_ops::to_padded_tensor::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt);
  }
}

namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor to_padded_tensor(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=::std::nullopt) {
    return at::_ops::to_padded_tensor::call(self, padding, output_size);
  }
}

// aten::_jagged_to_padded_dense_forward(Tensor values, Tensor[] offsets, SymInt[] max_lengths, float padding_value=0.0) -> Tensor
inline at::Tensor _jagged_to_padded_dense_forward(const at::Tensor & values, at::TensorList offsets, at::IntArrayRef max_lengths, double padding_value=0.0) {
    return at::_ops::_jagged_to_padded_dense_forward::call(values, offsets, c10::fromIntArrayRefSlow(max_lengths), padding_value);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _jagged_to_padded_dense_forward(const at::Tensor & values, at::TensorList offsets, at::IntArrayRef max_lengths, double padding_value=0.0) {
    return at::_ops::_jagged_to_padded_dense_forward::call(values, offsets, c10::fromIntArrayRefSlow(max_lengths), padding_value);
  }
}

// aten::_jagged_to_padded_dense_forward(Tensor values, Tensor[] offsets, SymInt[] max_lengths, float padding_value=0.0) -> Tensor
inline at::Tensor _jagged_to_padded_dense_forward_symint(const at::Tensor & values, at::TensorList offsets, c10::SymIntArrayRef max_lengths, double padding_value=0.0) {
    return at::_ops::_jagged_to_padded_dense_forward::call(values, offsets, max_lengths, padding_value);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _jagged_to_padded_dense_forward(const at::Tensor & values, at::TensorList offsets, c10::SymIntArrayRef max_lengths, double padding_value=0.0) {
    return at::_ops::_jagged_to_padded_dense_forward::call(values, offsets, max_lengths, padding_value);
  }
}

// aten::_padded_dense_to_jagged_forward(Tensor dense, Tensor[] offsets, SymInt? total_L=None) -> Tensor
inline at::Tensor _padded_dense_to_jagged_forward(const at::Tensor & dense, at::TensorList offsets, ::std::optional<int64_t> total_L=::std::nullopt) {
    return at::_ops::_padded_dense_to_jagged_forward::call(dense, offsets, total_L.has_value() ? ::std::make_optional(c10::SymInt(*total_L)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _padded_dense_to_jagged_forward(const at::Tensor & dense, at::TensorList offsets, ::std::optional<int64_t> total_L=::std::nullopt) {
    return at::_ops::_padded_dense_to_jagged_forward::call(dense, offsets, total_L.has_value() ? ::std::make_optional(c10::SymInt(*total_L)) : ::std::nullopt);
  }
}

// aten::_padded_dense_to_jagged_forward(Tensor dense, Tensor[] offsets, SymInt? total_L=None) -> Tensor
inline at::Tensor _padded_dense_to_jagged_forward_symint(const at::Tensor & dense, at::TensorList offsets, ::std::optional<c10::SymInt> total_L=::std::nullopt) {
    return at::_ops::_padded_dense_to_jagged_forward::call(dense, offsets, total_L);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _padded_dense_to_jagged_forward(const at::Tensor & dense, at::TensorList offsets, ::std::optional<c10::SymInt> total_L=::std::nullopt) {
    return at::_ops::_padded_dense_to_jagged_forward::call(dense, offsets, total_L);
  }
}

// aten::_nested_from_padded_tensor(Tensor padded, Tensor offsets, Tensor dummy, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, SymInt? sum_S=None) -> Tensor
inline at::Tensor _nested_from_padded_tensor(const at::Tensor & padded, const at::Tensor & offsets, const at::Tensor & dummy, int64_t ragged_idx=1, const ::std::optional<at::Tensor> & min_seqlen={}, const ::std::optional<at::Tensor> & max_seqlen={}, ::std::optional<int64_t> sum_S=::std::nullopt) {
    return at::_ops::_nested_from_padded_tensor::call(padded, offsets, dummy, ragged_idx, min_seqlen, max_seqlen, sum_S.has_value() ? ::std::make_optional(c10::SymInt(*sum_S)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _nested_from_padded_tensor(const at::Tensor & padded, const at::Tensor & offsets, const at::Tensor & dummy, int64_t ragged_idx=1, const ::std::optional<at::Tensor> & min_seqlen={}, const ::std::optional<at::Tensor> & max_seqlen={}, ::std::optional<int64_t> sum_S=::std::nullopt) {
    return at::_ops::_nested_from_padded_tensor::call(padded, offsets, dummy, ragged_idx, min_seqlen, max_seqlen, sum_S.has_value() ? ::std::make_optional(c10::SymInt(*sum_S)) : ::std::nullopt);
  }
}

// aten::_nested_from_padded_tensor(Tensor padded, Tensor offsets, Tensor dummy, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, SymInt? sum_S=None) -> Tensor
inline at::Tensor _nested_from_padded_tensor_symint(const at::Tensor & padded, const at::Tensor & offsets, const at::Tensor & dummy, int64_t ragged_idx=1, const ::std::optional<at::Tensor> & min_seqlen={}, const ::std::optional<at::Tensor> & max_seqlen={}, ::std::optional<c10::SymInt> sum_S=::std::nullopt) {
    return at::_ops::_nested_from_padded_tensor::call(padded, offsets, dummy, ragged_idx, min_seqlen, max_seqlen, sum_S);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _nested_from_padded_tensor(const at::Tensor & padded, const at::Tensor & offsets, const at::Tensor & dummy, int64_t ragged_idx=1, const ::std::optional<at::Tensor> & min_seqlen={}, const ::std::optional<at::Tensor> & max_seqlen={}, ::std::optional<c10::SymInt> sum_S=::std::nullopt) {
    return at::_ops::_nested_from_padded_tensor::call(padded, offsets, dummy, ragged_idx, min_seqlen, max_seqlen, sum_S);
  }
}

// aten::_nested_tensor_softmax_with_shape(Tensor self, Tensor query) -> Tensor
inline at::Tensor _nested_tensor_softmax_with_shape(const at::Tensor & self, const at::Tensor & query) {
    return at::_ops::_nested_tensor_softmax_with_shape::call(self, query);
}

// aten::_safe_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
inline at::Tensor _safe_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_safe_softmax::call(self, dim, dtype);
}

// aten::_transformer_encoder_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None) -> Tensor
inline at::Tensor _transformer_encoder_layer_fwd(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const ::std::optional<at::Tensor> & mask={}, ::std::optional<int64_t> mask_type=::std::nullopt) {
    return at::_ops::_transformer_encoder_layer_fwd::call(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type);
}

// aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _native_multi_head_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const ::std::optional<at::Tensor> & mask={}, bool need_weights=true, bool average_attn_weights=true, ::std::optional<int64_t> mask_type=::std::nullopt) {
    return at::_ops::_native_multi_head_attention::call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type);
}

// aten::scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, *, float? scale=None, bool enable_gqa=False) -> Tensor
inline at::Tensor scaled_dot_product_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_mask={}, double dropout_p=0.0, bool is_causal=false, ::std::optional<double> scale=::std::nullopt, bool enable_gqa=false) {
    return at::_ops::scaled_dot_product_attention::call(query, key, value, attn_mask, dropout_p, is_causal, scale, enable_gqa);
}

// aten::_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, *, float? scale=None, bool enable_gqa=False) -> int
inline int64_t _fused_sdp_choice(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_mask={}, double dropout_p=0.0, bool is_causal=false, ::std::optional<double> scale=::std::nullopt, bool enable_gqa=false) {
    return at::_ops::_fused_sdp_choice::call(query, key, value, attn_mask, dropout_p, is_causal, scale, enable_gqa);
}

// aten::_scaled_dot_product_attention_math(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, float? scale=None, bool enable_gqa=False) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_attention_math(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_mask={}, double dropout_p=0.0, bool is_causal=false, const ::std::optional<at::Tensor> & dropout_mask={}, ::std::optional<double> scale=::std::nullopt, bool enable_gqa=false) {
    return at::_ops::_scaled_dot_product_attention_math::call(query, key, value, attn_mask, dropout_p, is_causal, dropout_mask, scale, enable_gqa);
}

// aten::_scaled_dot_product_attention_math_for_mps(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, float? scale=None) -> (Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_attention_math_for_mps(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_mask={}, double dropout_p=0.0, bool is_causal=false, const ::std::optional<at::Tensor> & dropout_mask={}, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_attention_math_for_mps::call(query, key, value, attn_mask, dropout_p, is_causal, dropout_mask, scale);
}

// aten::_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p=0.0, bool is_causal=false, bool return_debug_mask=false, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_flash_attention::call(query, key, value, dropout_p, is_causal, return_debug_mask, scale);
}

// aten::_scaled_dot_product_flash_attention_for_cpu(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor output, Tensor logsumexp)
inline ::std::tuple<at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_for_cpu(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p=0.0, bool is_causal=false, const ::std::optional<at::Tensor> & attn_mask={}, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_flash_attention_for_cpu::call(query, key, value, dropout_p, is_causal, attn_mask, scale);
}

// aten::_scaled_dot_product_fused_attention_overrideable(Tensor query, Tensor key, Tensor value, Tensor? attn_bias=None, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_fused_attention_overrideable(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_bias={}, double dropout_p=0.0, bool is_causal=false, bool return_debug_mask=false, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_fused_attention_overrideable::call(query, key, value, attn_bias, dropout_p, is_causal, return_debug_mask, scale);
}

// aten::_scaled_dot_product_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
  }
}

// aten::_scaled_dot_product_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward_symint(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
  }
}

// aten::_scaled_dot_product_flash_attention_for_cpu_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, float dropout_p, bool is_causal, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_for_cpu_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, double dropout_p, bool is_causal, const ::std::optional<at::Tensor> & attn_mask={}, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_flash_attention_for_cpu_backward::call(grad_out, query, key, value, out, logsumexp, dropout_p, is_causal, attn_mask, scale);
}

// aten::_scaled_dot_product_fused_attention_overrideable_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor attn_bias, bool[4] grad_input_mask, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value, Tensor grad_attn_bias)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_fused_attention_overrideable_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, ::std::array<bool,4> grad_input_mask, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_fused_attention_overrideable_backward::call(grad_out, query, key, value, attn_bias, grad_input_mask, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_fused_attention_overrideable_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, ::std::array<bool,4> grad_input_mask, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_fused_attention_overrideable_backward::call(grad_out, query, key, value, attn_bias, grad_input_mask, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
  }
}

// aten::_scaled_dot_product_fused_attention_overrideable_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor attn_bias, bool[4] grad_input_mask, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value, Tensor grad_attn_bias)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_fused_attention_overrideable_backward_symint(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, ::std::array<bool,4> grad_input_mask, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_fused_attention_overrideable_backward::call(grad_out, query, key, value, attn_bias, grad_input_mask, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_fused_attention_overrideable_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, ::std::array<bool,4> grad_input_mask, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_fused_attention_overrideable_backward::call(grad_out, query, key, value, attn_bias, grad_input_mask, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
  }
}

// aten::_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, *, float? scale=None) -> (Tensor output, Tensor log_sumexp, Tensor philox_seed, Tensor philox_offset)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_efficient_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_bias, bool compute_log_sumexp, double dropout_p=0.0, bool is_causal=false, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_efficient_attention::call(query, key, value, attn_bias, compute_log_sumexp, dropout_p, is_causal, scale);
}

// aten::_scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor attn_bias, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, float dropout_p, bool[4] grad_input_mask, bool is_causal=False, *, float? scale=None) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_efficient_attention_backward(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, double dropout_p, ::std::array<bool,4> grad_input_mask, bool is_causal=false, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_efficient_attention_backward::call(grad_out_, query, key, value, attn_bias, out, logsumexp, philox_seed, philox_offset, dropout_p, grad_input_mask, is_causal, scale);
}

// aten::_scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_cudnn_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & attn_bias, bool compute_log_sumexp, double dropout_p=0.0, bool is_causal=false, bool return_debug_mask=false, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_cudnn_attention::call(query, key, value, attn_bias, compute_log_sumexp, dropout_p, is_causal, return_debug_mask, scale);
}

// aten::_scaled_dot_product_cudnn_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor attn_bias, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, *, float? scale=None) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_cudnn_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, const at::Tensor & attn_bias, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_cudnn_attention_backward::call(grad_out, query, key, value, out, logsumexp, philox_seed, philox_offset, attn_bias, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, scale);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_cudnn_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, const at::Tensor & attn_bias, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_cudnn_attention_backward::call(grad_out, query, key, value, out, logsumexp, philox_seed, philox_offset, attn_bias, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, scale);
  }
}

// aten::_scaled_dot_product_cudnn_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor attn_bias, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, *, float? scale=None) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_cudnn_attention_backward_symint(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, const at::Tensor & attn_bias, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_cudnn_attention_backward::call(grad_out, query, key, value, out, logsumexp, philox_seed, philox_offset, attn_bias, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, scale);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_cudnn_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, const at::Tensor & attn_bias, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, ::std::optional<double> scale=::std::nullopt) {
    return at::_ops::_scaled_dot_product_cudnn_attention_backward::call(grad_out, query, key, value, out, logsumexp, philox_seed, philox_offset, attn_bias, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, scale);
  }
}

// aten::_flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None, Tensor? seqused_k=None, Tensor? alibi_slopes=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _flash_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & cum_seq_q, const ::std::optional<at::Tensor> & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale=::std::nullopt, ::std::optional<int64_t> window_size_left=::std::nullopt, ::std::optional<int64_t> window_size_right=::std::nullopt, const ::std::optional<at::Tensor> & seqused_k={}, const ::std::optional<at::Tensor> & alibi_slopes={}) {
    return at::_ops::_flash_attention_forward::call(query, key, value, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, return_debug_mask, scale, window_size_left.has_value() ? ::std::make_optional(c10::SymInt(*window_size_left)) : ::std::nullopt, window_size_right.has_value() ? ::std::make_optional(c10::SymInt(*window_size_right)) : ::std::nullopt, seqused_k, alibi_slopes);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _flash_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & cum_seq_q, const ::std::optional<at::Tensor> & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale=::std::nullopt, ::std::optional<int64_t> window_size_left=::std::nullopt, ::std::optional<int64_t> window_size_right=::std::nullopt, const ::std::optional<at::Tensor> & seqused_k={}, const ::std::optional<at::Tensor> & alibi_slopes={}) {
    return at::_ops::_flash_attention_forward::call(query, key, value, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, return_debug_mask, scale, window_size_left.has_value() ? ::std::make_optional(c10::SymInt(*window_size_left)) : ::std::nullopt, window_size_right.has_value() ? ::std::make_optional(c10::SymInt(*window_size_right)) : ::std::nullopt, seqused_k, alibi_slopes);
  }
}

// aten::_flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None, Tensor? seqused_k=None, Tensor? alibi_slopes=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _flash_attention_forward_symint(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & cum_seq_q, const ::std::optional<at::Tensor> & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale=::std::nullopt, ::std::optional<c10::SymInt> window_size_left=::std::nullopt, ::std::optional<c10::SymInt> window_size_right=::std::nullopt, const ::std::optional<at::Tensor> & seqused_k={}, const ::std::optional<at::Tensor> & alibi_slopes={}) {
    return at::_ops::_flash_attention_forward::call(query, key, value, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, return_debug_mask, scale, window_size_left, window_size_right, seqused_k, alibi_slopes);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _flash_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & cum_seq_q, const ::std::optional<at::Tensor> & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, bool return_debug_mask, ::std::optional<double> scale=::std::nullopt, ::std::optional<c10::SymInt> window_size_left=::std::nullopt, ::std::optional<c10::SymInt> window_size_right=::std::nullopt, const ::std::optional<at::Tensor> & seqused_k={}, const ::std::optional<at::Tensor> & alibi_slopes={}) {
    return at::_ops::_flash_attention_forward::call(query, key, value, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, return_debug_mask, scale, window_size_left, window_size_right, seqused_k, alibi_slopes);
  }
}

// aten::_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt, ::std::optional<int64_t> window_size_left=::std::nullopt, ::std::optional<int64_t> window_size_right=::std::nullopt) {
    return at::_ops::_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale, window_size_left.has_value() ? ::std::make_optional(c10::SymInt(*window_size_left)) : ::std::nullopt, window_size_right.has_value() ? ::std::make_optional(c10::SymInt(*window_size_right)) : ::std::nullopt);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt, ::std::optional<int64_t> window_size_left=::std::nullopt, ::std::optional<int64_t> window_size_right=::std::nullopt) {
    return at::_ops::_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale, window_size_left.has_value() ? ::std::make_optional(c10::SymInt(*window_size_left)) : ::std::nullopt, window_size_right.has_value() ? ::std::make_optional(c10::SymInt(*window_size_right)) : ::std::nullopt);
  }
}

// aten::_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None, SymInt? window_size_left=None, SymInt? window_size_right=None) -> (Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _flash_attention_backward_symint(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt, ::std::optional<c10::SymInt> window_size_left=::std::nullopt, ::std::optional<c10::SymInt> window_size_right=::std::nullopt) {
    return at::_ops::_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale, window_size_left, window_size_right);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, ::std::optional<double> scale=::std::nullopt, ::std::optional<c10::SymInt> window_size_left=::std::nullopt, ::std::optional<c10::SymInt> window_size_right=::std::nullopt) {
    return at::_ops::_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale, window_size_left, window_size_right);
  }
}

// aten::_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt? max_seqlen_q, SymInt? max_seqlen_k, float dropout_p, int custom_mask_type, bool compute_log_sumexp=False, *, float? scale=None, Tensor? seqlen_k=None, int? window_size=None) -> (Tensor output, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, SymInt max_seqlen_batch_q, SymInt max_seqlen_batch_k)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt> _efficient_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, ::std::optional<int64_t> max_seqlen_q, ::std::optional<int64_t> max_seqlen_k, double dropout_p, int64_t custom_mask_type, bool compute_log_sumexp=false, ::std::optional<double> scale=::std::nullopt, const ::std::optional<at::Tensor> & seqlen_k={}, ::std::optional<int64_t> window_size=::std::nullopt) {
    return at::_ops::_efficient_attention_forward::call(query, key, value, bias, cu_seqlens_q, cu_seqlens_k, max_seqlen_q.has_value() ? ::std::make_optional(c10::SymInt(*max_seqlen_q)) : ::std::nullopt, max_seqlen_k.has_value() ? ::std::make_optional(c10::SymInt(*max_seqlen_k)) : ::std::nullopt, dropout_p, custom_mask_type, compute_log_sumexp, scale, seqlen_k, window_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt> _efficient_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, ::std::optional<int64_t> max_seqlen_q, ::std::optional<int64_t> max_seqlen_k, double dropout_p, int64_t custom_mask_type, bool compute_log_sumexp=false, ::std::optional<double> scale=::std::nullopt, const ::std::optional<at::Tensor> & seqlen_k={}, ::std::optional<int64_t> window_size=::std::nullopt) {
    return at::_ops::_efficient_attention_forward::call(query, key, value, bias, cu_seqlens_q, cu_seqlens_k, max_seqlen_q.has_value() ? ::std::make_optional(c10::SymInt(*max_seqlen_q)) : ::std::nullopt, max_seqlen_k.has_value() ? ::std::make_optional(c10::SymInt(*max_seqlen_k)) : ::std::nullopt, dropout_p, custom_mask_type, compute_log_sumexp, scale, seqlen_k, window_size);
  }
}

// aten::_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt? max_seqlen_q, SymInt? max_seqlen_k, float dropout_p, int custom_mask_type, bool compute_log_sumexp=False, *, float? scale=None, Tensor? seqlen_k=None, int? window_size=None) -> (Tensor output, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, SymInt max_seqlen_batch_q, SymInt max_seqlen_batch_k)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt> _efficient_attention_forward_symint(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, ::std::optional<c10::SymInt> max_seqlen_q, ::std::optional<c10::SymInt> max_seqlen_k, double dropout_p, int64_t custom_mask_type, bool compute_log_sumexp=false, ::std::optional<double> scale=::std::nullopt, const ::std::optional<at::Tensor> & seqlen_k={}, ::std::optional<int64_t> window_size=::std::nullopt) {
    return at::_ops::_efficient_attention_forward::call(query, key, value, bias, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, custom_mask_type, compute_log_sumexp, scale, seqlen_k, window_size);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt> _efficient_attention_forward(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, ::std::optional<c10::SymInt> max_seqlen_q, ::std::optional<c10::SymInt> max_seqlen_k, double dropout_p, int64_t custom_mask_type, bool compute_log_sumexp=false, ::std::optional<double> scale=::std::nullopt, const ::std::optional<at::Tensor> & seqlen_k={}, ::std::optional<int64_t> window_size=::std::nullopt) {
    return at::_ops::_efficient_attention_forward::call(query, key, value, bias, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, custom_mask_type, compute_log_sumexp, scale, seqlen_k, window_size);
  }
}

// aten::_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor out, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt max_seqlen_q, SymInt max_seqlen_k, Tensor logsumexp, float dropout_p, Tensor philox_seed, Tensor philox_offset, int custom_mask_type, bool bias_requires_grad, *, float? scale=None, int? num_splits_key=None, int? window_size=None, bool shared_storage_dqdkdv=False) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _efficient_attention_backward(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const at::Tensor & out, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, int64_t max_seqlen_q, int64_t max_seqlen_k, const at::Tensor & logsumexp, double dropout_p, const at::Tensor & philox_seed, const at::Tensor & philox_offset, int64_t custom_mask_type, bool bias_requires_grad, ::std::optional<double> scale=::std::nullopt, ::std::optional<int64_t> num_splits_key=::std::nullopt, ::std::optional<int64_t> window_size=::std::nullopt, bool shared_storage_dqdkdv=false) {
    return at::_ops::_efficient_attention_backward::call(grad_out_, query, key, value, bias, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, logsumexp, dropout_p, philox_seed, philox_offset, custom_mask_type, bias_requires_grad, scale, num_splits_key, window_size, shared_storage_dqdkdv);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _efficient_attention_backward(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const at::Tensor & out, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, int64_t max_seqlen_q, int64_t max_seqlen_k, const at::Tensor & logsumexp, double dropout_p, const at::Tensor & philox_seed, const at::Tensor & philox_offset, int64_t custom_mask_type, bool bias_requires_grad, ::std::optional<double> scale=::std::nullopt, ::std::optional<int64_t> num_splits_key=::std::nullopt, ::std::optional<int64_t> window_size=::std::nullopt, bool shared_storage_dqdkdv=false) {
    return at::_ops::_efficient_attention_backward::call(grad_out_, query, key, value, bias, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, logsumexp, dropout_p, philox_seed, philox_offset, custom_mask_type, bias_requires_grad, scale, num_splits_key, window_size, shared_storage_dqdkdv);
  }
}

// aten::_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor out, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt max_seqlen_q, SymInt max_seqlen_k, Tensor logsumexp, float dropout_p, Tensor philox_seed, Tensor philox_offset, int custom_mask_type, bool bias_requires_grad, *, float? scale=None, int? num_splits_key=None, int? window_size=None, bool shared_storage_dqdkdv=False) -> (Tensor, Tensor, Tensor, Tensor)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _efficient_attention_backward_symint(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const at::Tensor & out, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, c10::SymInt max_seqlen_q, c10::SymInt max_seqlen_k, const at::Tensor & logsumexp, double dropout_p, const at::Tensor & philox_seed, const at::Tensor & philox_offset, int64_t custom_mask_type, bool bias_requires_grad, ::std::optional<double> scale=::std::nullopt, ::std::optional<int64_t> num_splits_key=::std::nullopt, ::std::optional<int64_t> window_size=::std::nullopt, bool shared_storage_dqdkdv=false) {
    return at::_ops::_efficient_attention_backward::call(grad_out_, query, key, value, bias, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, logsumexp, dropout_p, philox_seed, philox_offset, custom_mask_type, bias_requires_grad, scale, num_splits_key, window_size, shared_storage_dqdkdv);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _efficient_attention_backward(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const ::std::optional<at::Tensor> & bias, const at::Tensor & out, const ::std::optional<at::Tensor> & cu_seqlens_q, const ::std::optional<at::Tensor> & cu_seqlens_k, c10::SymInt max_seqlen_q, c10::SymInt max_seqlen_k, const at::Tensor & logsumexp, double dropout_p, const at::Tensor & philox_seed, const at::Tensor & philox_offset, int64_t custom_mask_type, bool bias_requires_grad, ::std::optional<double> scale=::std::nullopt, ::std::optional<int64_t> num_splits_key=::std::nullopt, ::std::optional<int64_t> window_size=::std::nullopt, bool shared_storage_dqdkdv=false) {
    return at::_ops::_efficient_attention_backward::call(grad_out_, query, key, value, bias, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, logsumexp, dropout_p, philox_seed, philox_offset, custom_mask_type, bias_requires_grad, scale, num_splits_key, window_size, shared_storage_dqdkdv);
  }
}

// aten::_triton_scaled_dot_attention(Tensor q, Tensor k, Tensor v, float dropout_p=0.0) -> Tensor
inline at::Tensor _triton_scaled_dot_attention(const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p=0.0) {
    return at::_ops::_triton_scaled_dot_attention::call(q, k, v, dropout_p);
}

// aten::_fill_mem_eff_dropout_mask_(Tensor(a!) self, float dropout_p, int seed, int offset) -> Tensor(a!)
inline at::Tensor & _fill_mem_eff_dropout_mask_(at::Tensor & self, double dropout_p, int64_t seed, int64_t offset) {
    return at::_ops::_fill_mem_eff_dropout_mask_::call(self, dropout_p, seed, offset);
}

// aten::_triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None) -> Tensor
inline at::Tensor _triton_multi_head_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const ::std::optional<at::Tensor> & mask={}) {
    return at::_ops::_triton_multi_head_attention::call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask);
}

// aten::special_airy_ai(Tensor x) -> Tensor
inline at::Tensor special_airy_ai(const at::Tensor & x) {
    return at::_ops::special_airy_ai::call(x);
}

// aten::special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_airy_ai_out(at::Tensor & out, const at::Tensor & x) {
    return at::_ops::special_airy_ai_out::call(x, out);
}
// aten::special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_airy_ai_outf(const at::Tensor & x, at::Tensor & out) {
    return at::_ops::special_airy_ai_out::call(x, out);
}

// aten::special_bessel_j0(Tensor self) -> Tensor
inline at::Tensor special_bessel_j0(const at::Tensor & self) {
    return at::_ops::special_bessel_j0::call(self);
}

// aten::special_bessel_j0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_bessel_j0_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_bessel_j0_out::call(self, out);
}
// aten::special_bessel_j0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_bessel_j0_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_bessel_j0_out::call(self, out);
}

// aten::special_bessel_j1(Tensor self) -> Tensor
inline at::Tensor special_bessel_j1(const at::Tensor & self) {
    return at::_ops::special_bessel_j1::call(self);
}

// aten::special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_bessel_j1_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_bessel_j1_out::call(self, out);
}
// aten::special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_bessel_j1_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_bessel_j1_out::call(self, out);
}

// aten::special_bessel_y0(Tensor self) -> Tensor
inline at::Tensor special_bessel_y0(const at::Tensor & self) {
    return at::_ops::special_bessel_y0::call(self);
}

// aten::special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_bessel_y0_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_bessel_y0_out::call(self, out);
}
// aten::special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_bessel_y0_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_bessel_y0_out::call(self, out);
}

// aten::special_bessel_y1(Tensor self) -> Tensor
inline at::Tensor special_bessel_y1(const at::Tensor & self) {
    return at::_ops::special_bessel_y1::call(self);
}

// aten::special_bessel_y1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_bessel_y1_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_bessel_y1_out::call(self, out);
}
// aten::special_bessel_y1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_bessel_y1_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_bessel_y1_out::call(self, out);
}

// aten::special_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_t::call(x, n);
}

// aten::special_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_t(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_t_x_scalar::call(x, n);
}

// aten::special_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_t(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_chebyshev_polynomial_t_n_scalar::call(x, n);
}

// aten::special_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_t_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_t_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_t_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_t_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_t_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_t_x_scalar_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_t_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_t_x_scalar_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_t_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_chebyshev_polynomial_t_n_scalar_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_t_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_t_n_scalar_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_u::call(x, n);
}

// aten::special_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_u(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_u_x_scalar::call(x, n);
}

// aten::special_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_u(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_chebyshev_polynomial_u_n_scalar::call(x, n);
}

// aten::special_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_u_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_u_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_u_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_u_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_u_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_u_x_scalar_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_u_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_u_x_scalar_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_u_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_chebyshev_polynomial_u_n_scalar_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_u_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_u_n_scalar_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_v::call(x, n);
}

// aten::special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_v(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_v_x_scalar::call(x, n);
}

// aten::special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_v(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_chebyshev_polynomial_v_n_scalar::call(x, n);
}

// aten::special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_v_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_v_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_v_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_v_x_scalar_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_v_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_v_x_scalar_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_chebyshev_polynomial_v_n_scalar_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_v_n_scalar_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_w::call(x, n);
}

// aten::special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_w(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_w_x_scalar::call(x, n);
}

// aten::special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_chebyshev_polynomial_w(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_chebyshev_polynomial_w_n_scalar::call(x, n);
}

// aten::special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_w_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_w_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_w_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_chebyshev_polynomial_w_x_scalar_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_w_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_w_x_scalar_out::call(x, n, out);
}

// aten::special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_chebyshev_polynomial_w_n_scalar_out::call(x, n, out);
}
// aten::special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_chebyshev_polynomial_w_n_scalar_out::call(x, n, out);
}

// aten::special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_hermite_polynomial_h::call(x, n);
}

// aten::special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_hermite_polynomial_h(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_hermite_polynomial_h_x_scalar::call(x, n);
}

// aten::special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_hermite_polynomial_h_n_scalar::call(x, n);
}

// aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_hermite_polynomial_h_out::call(x, n, out);
}
// aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_hermite_polynomial_h_out::call(x, n, out);
}

// aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_hermite_polynomial_h_x_scalar_out::call(x, n, out);
}
// aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_h_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_hermite_polynomial_h_x_scalar_out::call(x, n, out);
}

// aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_hermite_polynomial_h_n_scalar_out::call(x, n, out);
}
// aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_hermite_polynomial_h_n_scalar_out::call(x, n, out);
}

// aten::special_hermite_polynomial_he(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_hermite_polynomial_he::call(x, n);
}

// aten::special_hermite_polynomial_he.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_hermite_polynomial_he(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_hermite_polynomial_he_x_scalar::call(x, n);
}

// aten::special_hermite_polynomial_he.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_hermite_polynomial_he_n_scalar::call(x, n);
}

// aten::special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_hermite_polynomial_he_out::call(x, n, out);
}
// aten::special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_hermite_polynomial_he_out::call(x, n, out);
}

// aten::special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_hermite_polynomial_he_x_scalar_out::call(x, n, out);
}
// aten::special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_he_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_hermite_polynomial_he_x_scalar_out::call(x, n, out);
}

// aten::special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_hermite_polynomial_he_n_scalar_out::call(x, n, out);
}
// aten::special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_hermite_polynomial_he_n_scalar_out::call(x, n, out);
}

// aten::special_laguerre_polynomial_l(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_laguerre_polynomial_l::call(x, n);
}

// aten::special_laguerre_polynomial_l.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_laguerre_polynomial_l(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_laguerre_polynomial_l_x_scalar::call(x, n);
}

// aten::special_laguerre_polynomial_l.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_laguerre_polynomial_l_n_scalar::call(x, n);
}

// aten::special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_laguerre_polynomial_l_out::call(x, n, out);
}
// aten::special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_laguerre_polynomial_l_out::call(x, n, out);
}

// aten::special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_laguerre_polynomial_l_x_scalar_out::call(x, n, out);
}
// aten::special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_laguerre_polynomial_l_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_laguerre_polynomial_l_x_scalar_out::call(x, n, out);
}

// aten::special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_laguerre_polynomial_l_n_scalar_out::call(x, n, out);
}
// aten::special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_laguerre_polynomial_l_n_scalar_out::call(x, n, out);
}

// aten::special_legendre_polynomial_p(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_legendre_polynomial_p::call(x, n);
}

// aten::special_legendre_polynomial_p.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_legendre_polynomial_p(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_legendre_polynomial_p_x_scalar::call(x, n);
}

// aten::special_legendre_polynomial_p.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_legendre_polynomial_p_n_scalar::call(x, n);
}

// aten::special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_legendre_polynomial_p_out::call(x, n, out);
}
// aten::special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_legendre_polynomial_p_out::call(x, n, out);
}

// aten::special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_legendre_polynomial_p_x_scalar_out::call(x, n, out);
}
// aten::special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_legendre_polynomial_p_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_legendre_polynomial_p_x_scalar_out::call(x, n, out);
}

// aten::special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_legendre_polynomial_p_n_scalar_out::call(x, n, out);
}
// aten::special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_legendre_polynomial_p_n_scalar_out::call(x, n, out);
}

// aten::special_modified_bessel_i0(Tensor self) -> Tensor
inline at::Tensor special_modified_bessel_i0(const at::Tensor & self) {
    return at::_ops::special_modified_bessel_i0::call(self);
}

// aten::special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_modified_bessel_i0_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_modified_bessel_i0_out::call(self, out);
}
// aten::special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_modified_bessel_i0_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_modified_bessel_i0_out::call(self, out);
}

// aten::special_modified_bessel_i1(Tensor self) -> Tensor
inline at::Tensor special_modified_bessel_i1(const at::Tensor & self) {
    return at::_ops::special_modified_bessel_i1::call(self);
}

// aten::special_modified_bessel_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_modified_bessel_i1_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_modified_bessel_i1_out::call(self, out);
}
// aten::special_modified_bessel_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_modified_bessel_i1_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_modified_bessel_i1_out::call(self, out);
}

// aten::special_modified_bessel_k0(Tensor self) -> Tensor
inline at::Tensor special_modified_bessel_k0(const at::Tensor & self) {
    return at::_ops::special_modified_bessel_k0::call(self);
}

// aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_modified_bessel_k0_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_modified_bessel_k0_out::call(self, out);
}
// aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_modified_bessel_k0_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_modified_bessel_k0_out::call(self, out);
}

// aten::special_modified_bessel_k1(Tensor self) -> Tensor
inline at::Tensor special_modified_bessel_k1(const at::Tensor & self) {
    return at::_ops::special_modified_bessel_k1::call(self);
}

// aten::special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_modified_bessel_k1_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::special_modified_bessel_k1_out::call(self, out);
}
// aten::special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_modified_bessel_k1_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::special_modified_bessel_k1_out::call(self, out);
}

// aten::special_scaled_modified_bessel_k0(Tensor x) -> Tensor
inline at::Tensor special_scaled_modified_bessel_k0(const at::Tensor & x) {
    return at::_ops::special_scaled_modified_bessel_k0::call(x);
}

// aten::special_scaled_modified_bessel_k0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_scaled_modified_bessel_k0_out(at::Tensor & out, const at::Tensor & x) {
    return at::_ops::special_scaled_modified_bessel_k0_out::call(x, out);
}
// aten::special_scaled_modified_bessel_k0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_scaled_modified_bessel_k0_outf(const at::Tensor & x, at::Tensor & out) {
    return at::_ops::special_scaled_modified_bessel_k0_out::call(x, out);
}

// aten::special_scaled_modified_bessel_k1(Tensor x) -> Tensor
inline at::Tensor special_scaled_modified_bessel_k1(const at::Tensor & x) {
    return at::_ops::special_scaled_modified_bessel_k1::call(x);
}

// aten::special_scaled_modified_bessel_k1.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_scaled_modified_bessel_k1_out(at::Tensor & out, const at::Tensor & x) {
    return at::_ops::special_scaled_modified_bessel_k1_out::call(x, out);
}
// aten::special_scaled_modified_bessel_k1.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_scaled_modified_bessel_k1_outf(const at::Tensor & x, at::Tensor & out) {
    return at::_ops::special_scaled_modified_bessel_k1_out::call(x, out);
}

// aten::special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_t::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_t(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_t_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_t_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_t_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_t_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_t_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_t_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_t_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_t_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_u::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_u(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_u_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_u_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_u_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_u_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_u_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_u_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_u_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_u_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_v::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_v(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_v_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_v_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_w::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_w(const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor
inline at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar::call(x, n);
}

// aten::special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_w_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_w_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar_out::call(x, n, out);
}

// aten::special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
    return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar_out::call(x, n, out);
}
// aten::special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
    return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar_out::call(x, n, out);
}

// aten::special_spherical_bessel_j0(Tensor x) -> Tensor
inline at::Tensor special_spherical_bessel_j0(const at::Tensor & x) {
    return at::_ops::special_spherical_bessel_j0::call(x);
}

// aten::special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_spherical_bessel_j0_out(at::Tensor & out, const at::Tensor & x) {
    return at::_ops::special_spherical_bessel_j0_out::call(x, out);
}
// aten::special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & special_spherical_bessel_j0_outf(const at::Tensor & x, at::Tensor & out) {
    return at::_ops::special_spherical_bessel_j0_out::call(x, out);
}

// aten::_foobar(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True) -> Tensor
inline at::Tensor _foobar(const at::Tensor & self, bool arg1=true, bool arg2=true, bool arg3=true) {
    return at::_ops::_foobar::call(self, arg1, arg2, arg3);
}

// aten::_fused_adam_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
inline void _fused_adam_(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adam_::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}

// aten::_fused_adam_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
inline void _fused_adam_(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adam__tensor_lr::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}

// aten::_fused_adamw_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
inline void _fused_adamw_(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adamw_::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}

// aten::_fused_adamw_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
inline void _fused_adamw_(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adamw__tensor_lr::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}

// aten::_fused_sgd_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
inline void _fused_sgd_(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_sgd_::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
}

// aten::_fused_sgd_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
inline void _fused_sgd_(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_sgd__tensor_lr::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
}

// aten::_fused_adagrad_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] state_sums, Tensor(d!)[] state_steps, *, float lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> ()
inline void _fused_adagrad_(at::TensorList self, at::TensorList grads, at::TensorList state_sums, at::TensorList state_steps, double lr, double lr_decay, double weight_decay, double eps, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adagrad_::call(self, grads, state_sums, state_steps, lr, lr_decay, weight_decay, eps, maximize, grad_scale, found_inf);
}

// aten::_propagate_xla_data(Tensor input, Tensor output) -> ()
inline void _propagate_xla_data(const at::Tensor & input, const at::Tensor & output) {
    return at::_ops::_propagate_xla_data::call(input, output);
}

// aten::_new_zeros_with_same_feature_meta.out(Tensor self, Tensor other, *, int self_num_batch_dims=0, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _new_zeros_with_same_feature_meta_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims=0) {
    return at::_ops::_new_zeros_with_same_feature_meta_out::call(self, other, self_num_batch_dims, out);
}
// aten::_new_zeros_with_same_feature_meta.out(Tensor self, Tensor other, *, int self_num_batch_dims=0, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _new_zeros_with_same_feature_meta_outf(const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims, at::Tensor & out) {
    return at::_ops::_new_zeros_with_same_feature_meta_out::call(self, other, self_num_batch_dims, out);
}

// aten::_cudnn_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _cudnn_ctc_loss_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity) {
    return at::_ops::_cudnn_ctc_loss_out::call(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity, out0, out1);
}
// aten::_cudnn_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _cudnn_ctc_loss_outf(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_cudnn_ctc_loss_out::call(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity, out0, out1);
}

// aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cudnn_rnn_flatten_weight_out(at::Tensor & out, at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional) {
    return at::_ops::_cudnn_rnn_flatten_weight_out::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _cudnn_rnn_flatten_weight_out(at::Tensor & out, at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional) {
    return at::_ops::_cudnn_rnn_flatten_weight_out::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out);
  }
}

// aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cudnn_rnn_flatten_weight_outf(at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out) {
    return at::_ops::_cudnn_rnn_flatten_weight_out::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _cudnn_rnn_flatten_weight_outf(at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out) {
    return at::_ops::_cudnn_rnn_flatten_weight_out::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out);
  }
}

// aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cudnn_rnn_flatten_weight_symint_out(at::Tensor & out, at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) {
    return at::_ops::_cudnn_rnn_flatten_weight_out::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _cudnn_rnn_flatten_weight_out(at::Tensor & out, at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) {
    return at::_ops::_cudnn_rnn_flatten_weight_out::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out);
  }
}

// aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cudnn_rnn_flatten_weight_symint_outf(at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out) {
    return at::_ops::_cudnn_rnn_flatten_weight_out::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _cudnn_rnn_flatten_weight_outf(at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out) {
    return at::_ops::_cudnn_rnn_flatten_weight_out::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out);
  }
}

// aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4);
  }
}

// aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4);
  }
}

// aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
  }
}

// aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_symint_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _cudnn_rnn_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const ::std::optional<at::Tensor> & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
    return at::_ops::_cudnn_rnn_out::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
  }
}

// aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()
inline void _cudnn_rnn_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask, out0, out1, out2, out3);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void _cudnn_rnn_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask, out0, out1, out2, out3);
  }
}

// aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()
inline void _cudnn_rnn_backward_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) {
    return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask, out0, out1, out2, out3);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void _cudnn_rnn_backward_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) {
    return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask, out0, out1, out2, out3);
  }
}

// aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()
inline void _cudnn_rnn_backward_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void _cudnn_rnn_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3);
  }
}

// aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()
inline void _cudnn_rnn_backward_symint_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) {
    return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void _cudnn_rnn_backward_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) {
    return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3);
  }
}

// aten::_cudnn_init_dropout_state.out(float dropout, bool train, int dropout_seed, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cudnn_init_dropout_state_out(at::Tensor & out, double dropout, bool train, int64_t dropout_seed) {
    return at::_ops::_cudnn_init_dropout_state_out::call(dropout, train, dropout_seed, out);
}
// aten::_cudnn_init_dropout_state.out(float dropout, bool train, int dropout_seed, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cudnn_init_dropout_state_outf(double dropout, bool train, int64_t dropout_seed, at::Tensor & out) {
    return at::_ops::_cudnn_init_dropout_state_out::call(dropout, train, dropout_seed, out);
}

// aten::_fused_dropout.out(Tensor self, float p, Generator? generator=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _fused_dropout_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, double p, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::_fused_dropout_out::call(self, p, generator, out0, out1);
}
// aten::_fused_dropout.out(Tensor self, float p, Generator? generator=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _fused_dropout_outf(const at::Tensor & self, double p, ::std::optional<at::Generator> generator, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_fused_dropout_out::call(self, p, generator, out0, out1);
}

// aten::_masked_scale.out(Tensor self, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _masked_scale_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, double scale) {
    return at::_ops::_masked_scale_out::call(self, mask, scale, out);
}
// aten::_masked_scale.out(Tensor self, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _masked_scale_outf(const at::Tensor & self, const at::Tensor & mask, double scale, at::Tensor & out) {
    return at::_ops::_masked_scale_out::call(self, mask, scale, out);
}

// aten::native_dropout.out(Tensor input, float p, bool? train, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> native_dropout_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, double p, ::std::optional<bool> train) {
    return at::_ops::native_dropout_out::call(input, p, train, out0, out1);
}
// aten::native_dropout.out(Tensor input, float p, bool? train, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> native_dropout_outf(const at::Tensor & input, double p, ::std::optional<bool> train, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::native_dropout_out::call(input, p, train, out0, out1);
}

// aten::native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & native_dropout_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & mask, double scale) {
    return at::_ops::native_dropout_backward_out::call(grad_output, mask, scale, out);
}
// aten::native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & native_dropout_backward_outf(const at::Tensor & grad_output, const at::Tensor & mask, double scale, at::Tensor & out) {
    return at::_ops::native_dropout_backward_out::call(grad_output, mask, scale, out);
}

// aten::_conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _conj_physical_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_conj_physical_out::call(self, out);
}
// aten::_conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _conj_physical_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_conj_physical_out::call(self, out);
}

// aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::_add_relu_Scalar_out::call(self, other, alpha, out);
}
// aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _add_relu_outf(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::_add_relu_Scalar_out::call(self, other, alpha, out);
}

// aten::add.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & add_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::add_Scalar_out::call(self, other, alpha, out);
}
// aten::add.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & add_outf(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::add_Scalar_out::call(self, other, alpha, out);
}

// aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & affine_grid_generator_out(at::Tensor & out, const at::Tensor & theta, at::IntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator_out::call(theta, c10::fromIntArrayRefSlow(size), align_corners, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & affine_grid_generator_out(at::Tensor & out, const at::Tensor & theta, at::IntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator_out::call(theta, c10::fromIntArrayRefSlow(size), align_corners, out);
  }
}

// aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & affine_grid_generator_outf(const at::Tensor & theta, at::IntArrayRef size, bool align_corners, at::Tensor & out) {
    return at::_ops::affine_grid_generator_out::call(theta, c10::fromIntArrayRefSlow(size), align_corners, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & affine_grid_generator_outf(const at::Tensor & theta, at::IntArrayRef size, bool align_corners, at::Tensor & out) {
    return at::_ops::affine_grid_generator_out::call(theta, c10::fromIntArrayRefSlow(size), align_corners, out);
  }
}

// aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & affine_grid_generator_symint_out(at::Tensor & out, const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator_out::call(theta, size, align_corners, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & affine_grid_generator_out(at::Tensor & out, const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners) {
    return at::_ops::affine_grid_generator_out::call(theta, size, align_corners, out);
  }
}

// aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & affine_grid_generator_symint_outf(const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners, at::Tensor & out) {
    return at::_ops::affine_grid_generator_out::call(theta, size, align_corners, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & affine_grid_generator_outf(const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners, at::Tensor & out) {
    return at::_ops::affine_grid_generator_out::call(theta, size, align_corners, out);
  }
}

// aten::_test_functorch_fallback.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_functorch_fallback_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::_test_functorch_fallback_out::call(self, other, out);
}
// aten::_test_functorch_fallback.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_functorch_fallback_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::_test_functorch_fallback_out::call(self, other, out);
}

// aten::bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bartlett_window_out(at::Tensor & out, int64_t window_length) {
    return at::_ops::bartlett_window_out::call(window_length, out);
}
// aten::bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bartlett_window_outf(int64_t window_length, at::Tensor & out) {
    return at::_ops::bartlett_window_out::call(window_length, out);
}

// aten::bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bartlett_window_out(at::Tensor & out, int64_t window_length, bool periodic) {
    return at::_ops::bartlett_window_periodic_out::call(window_length, periodic, out);
}
// aten::bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bartlett_window_outf(int64_t window_length, bool periodic, at::Tensor & out) {
    return at::_ops::bartlett_window_periodic_out::call(window_length, periodic, out);
}

// aten::quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantized_batch_norm_out(at::Tensor & out, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point) {
    return at::_ops::quantized_batch_norm_out::call(input, weight, bias, mean, var, eps, output_scale, output_zero_point, out);
}
// aten::quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantized_batch_norm_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point, at::Tensor & out) {
    return at::_ops::quantized_batch_norm_out::call(input, weight, bias, mean, var, eps, output_scale, output_zero_point, out);
}

// aten::bernoulli.Tensor_out(Tensor self, Tensor p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bernoulli_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & p, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::bernoulli_Tensor_out::call(self, p, generator, out);
}
// aten::bernoulli.Tensor_out(Tensor self, Tensor p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bernoulli_outf(const at::Tensor & self, const at::Tensor & p, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::bernoulli_Tensor_out::call(self, p, generator, out);
}

// aten::bernoulli.Tensor(Tensor self, Tensor p, *, Generator? generator=None) -> Tensor
inline at::Tensor bernoulli(const at::Tensor & self, const at::Tensor & p, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::bernoulli_Tensor::call(self, p, generator);
}

// aten::bernoulli.float_out(Tensor self, float p=0.5, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bernoulli_out(at::Tensor & out, const at::Tensor & self, double p=0.5, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::bernoulli_float_out::call(self, p, generator, out);
}
// aten::bernoulli.float_out(Tensor self, float p=0.5, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bernoulli_outf(const at::Tensor & self, double p, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::bernoulli_float_out::call(self, p, generator, out);
}

// aten::binary_cross_entropy_with_logits.out(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & binary_cross_entropy_with_logits_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight={}, const ::std::optional<at::Tensor> & pos_weight={}, int64_t reduction=at::Reduction::Mean) {
    return at::_ops::binary_cross_entropy_with_logits_out::call(self, target, weight, pos_weight, reduction, out);
}
// aten::binary_cross_entropy_with_logits.out(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & binary_cross_entropy_with_logits_outf(const at::Tensor & self, const at::Tensor & target, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & pos_weight, int64_t reduction, at::Tensor & out) {
    return at::_ops::binary_cross_entropy_with_logits_out::call(self, target, weight, pos_weight, reduction, out);
}

// aten::bincount.out(Tensor self, Tensor? weights=None, int minlength=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bincount_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Tensor> & weights={}, int64_t minlength=0) {
    return at::_ops::bincount_out::call(self, weights, minlength, out);
}
// aten::bincount.out(Tensor self, Tensor? weights=None, int minlength=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bincount_outf(const at::Tensor & self, const ::std::optional<at::Tensor> & weights, int64_t minlength, at::Tensor & out) {
    return at::_ops::bincount_out::call(self, weights, minlength, out);
}

// aten::blackman_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & blackman_window_out(at::Tensor & out, int64_t window_length) {
    return at::_ops::blackman_window_out::call(window_length, out);
}
// aten::blackman_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & blackman_window_outf(int64_t window_length, at::Tensor & out) {
    return at::_ops::blackman_window_out::call(window_length, out);
}

// aten::blackman_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & blackman_window_out(at::Tensor & out, int64_t window_length, bool periodic) {
    return at::_ops::blackman_window_periodic_out::call(window_length, periodic, out);
}
// aten::blackman_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & blackman_window_outf(int64_t window_length, bool periodic, at::Tensor & out) {
    return at::_ops::blackman_window_periodic_out::call(window_length, periodic, out);
}

// aten::block_diag.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & block_diag_out(at::Tensor & out, at::TensorList tensors) {
    return at::_ops::block_diag_out::call(tensors, out);
}
// aten::block_diag.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & block_diag_outf(at::TensorList tensors, at::Tensor & out) {
    return at::_ops::block_diag_out::call(tensors, out);
}

// aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & constant_pad_nd_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value=0) {
    return at::_ops::constant_pad_nd_out::call(self, c10::fromIntArrayRefSlow(pad), value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & constant_pad_nd_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value=0) {
    return at::_ops::constant_pad_nd_out::call(self, c10::fromIntArrayRefSlow(pad), value, out);
  }
}

// aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & constant_pad_nd_outf(const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::constant_pad_nd_out::call(self, c10::fromIntArrayRefSlow(pad), value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & constant_pad_nd_outf(const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::constant_pad_nd_out::call(self, c10::fromIntArrayRefSlow(pad), value, out);
  }
}

// aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & constant_pad_nd_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value=0) {
    return at::_ops::constant_pad_nd_out::call(self, pad, value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & constant_pad_nd_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value=0) {
    return at::_ops::constant_pad_nd_out::call(self, pad, value, out);
  }
}

// aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & constant_pad_nd_symint_outf(const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::constant_pad_nd_out::call(self, pad, value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & constant_pad_nd_outf(const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::constant_pad_nd_out::call(self, pad, value, out);
  }
}

// aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) {
    return at::_ops::convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) {
    return at::_ops::convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out);
  }
}

// aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, at::Tensor & out) {
    return at::_ops::convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, at::Tensor & out) {
    return at::_ops::convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out);
  }
}

// aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & convolution_symint_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) {
    return at::_ops::convolution_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) {
    return at::_ops::convolution_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out);
  }
}

// aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & convolution_symint_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::convolution_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::convolution_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out);
  }
}

// aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_out::call(grad_output, input, weight, bias_sizes.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : ::std::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_out::call(grad_output, input, weight, bias_sizes.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : ::std::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2);
  }
}

// aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::convolution_backward_out::call(grad_output, input, weight, bias_sizes.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : ::std::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::convolution_backward_out::call(grad_output, input, weight, bias_sizes.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : ::std::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2);
  }
}

// aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_out::call(grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_out::call(grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2);
  }
}

// aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::convolution_backward_out::call(grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::convolution_backward_out::call(grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2);
  }
}

// aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & convolution_overrideable_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) {
    return at::_ops::convolution_overrideable_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & convolution_overrideable_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) {
    return at::_ops::convolution_overrideable_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out);
  }
}

// aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & convolution_overrideable_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, at::Tensor & out) {
    return at::_ops::convolution_overrideable_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & convolution_overrideable_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, at::Tensor & out) {
    return at::_ops::convolution_overrideable_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out);
  }
}

// aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & convolution_overrideable_symint_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) {
    return at::_ops::convolution_overrideable_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & convolution_overrideable_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) {
    return at::_ops::convolution_overrideable_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out);
  }
}

// aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & convolution_overrideable_symint_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::convolution_overrideable_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & convolution_overrideable_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::convolution_overrideable_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out);
  }
}

// aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_overrideable_out::call(grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_overrideable_out::call(grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2);
  }
}

// aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::convolution_backward_overrideable_out::call(grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::convolution_backward_overrideable_out::call(grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2);
  }
}

// aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_overrideable_out::call(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::convolution_backward_overrideable_out::call(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2);
  }
}

// aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_symint_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::convolution_backward_overrideable_out::call(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> convolution_backward_overrideable_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::convolution_backward_overrideable_out::call(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2);
  }
}

// aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) {
    return at::_ops::_convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) {
    return at::_ops::_convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out);
  }
}

// aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, at::Tensor & out) {
    return at::_ops::_convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, at::Tensor & out) {
    return at::_ops::_convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out);
  }
}

// aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _convolution_symint_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) {
    return at::_ops::_convolution_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) {
    return at::_ops::_convolution_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out);
  }
}

// aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _convolution_symint_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, at::Tensor & out) {
    return at::_ops::_convolution_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, at::Tensor & out) {
    return at::_ops::_convolution_out::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out);
  }
}

// aten::conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & conv_tbc_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad=0) {
    return at::_ops::conv_tbc_out::call(self, weight, bias, pad, out);
}
// aten::conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & conv_tbc_outf(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad, at::Tensor & out) {
    return at::_ops::conv_tbc_out::call(self, weight, bias, pad, out);
}

// aten::copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & copy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, bool non_blocking=false) {
    return at::_ops::copy_out::call(self, src, non_blocking, out);
}
// aten::copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & copy_outf(const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out) {
    return at::_ops::copy_out::call(self, src, non_blocking, out);
}

// aten::_copy_from.out(Tensor self, Tensor dst, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _copy_from_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & dst, bool non_blocking=false) {
    return at::_ops::_copy_from_out::call(self, dst, non_blocking, out);
}
// aten::_copy_from.out(Tensor self, Tensor dst, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _copy_from_outf(const at::Tensor & self, const at::Tensor & dst, bool non_blocking, at::Tensor & out) {
    return at::_ops::_copy_from_out::call(self, dst, non_blocking, out);
}

// aten::_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _copy_from_and_resize_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & dst) {
    return at::_ops::_copy_from_and_resize_out::call(self, dst, out);
}
// aten::_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _copy_from_and_resize_outf(const at::Tensor & self, const at::Tensor & dst, at::Tensor & out) {
    return at::_ops::_copy_from_and_resize_out::call(self, dst, out);
}

// aten::count_nonzero.dim_IntList_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & count_nonzero_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::count_nonzero_dim_IntList_out::call(self, dim, out);
}
// aten::count_nonzero.dim_IntList_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & count_nonzero_outf(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) {
    return at::_ops::count_nonzero_dim_IntList_out::call(self, dim, out);
}

// aten::count_nonzero.out(Tensor self, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & count_nonzero_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::count_nonzero_out::call(self, dim, out);
}
// aten::count_nonzero.out(Tensor self, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & count_nonzero_outf(const at::Tensor & self, ::std::optional<int64_t> dim, at::Tensor & out) {
    return at::_ops::count_nonzero_out::call(self, dim, out);
}

// aten::cudnn_affine_grid_generator.out(Tensor theta, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_affine_grid_generator_out(at::Tensor & out, const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W) {
    return at::_ops::cudnn_affine_grid_generator_out::call(theta, N, C, H, W, out);
}
// aten::cudnn_affine_grid_generator.out(Tensor theta, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_affine_grid_generator_outf(const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out) {
    return at::_ops::cudnn_affine_grid_generator_out::call(theta, N, C, H, W, out);
}

// aten::cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_affine_grid_generator_backward_out(at::Tensor & out, const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) {
    return at::_ops::cudnn_affine_grid_generator_backward_out::call(grad, N, C, H, W, out);
}
// aten::cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_affine_grid_generator_backward_outf(const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out) {
    return at::_ops::cudnn_affine_grid_generator_backward_out::call(grad, N, C, H, W, out);
}

// aten::cudnn_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> cudnn_batch_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon) {
    return at::_ops::cudnn_batch_norm_out::call(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, out0, out1, out2, out3);
}
// aten::cudnn_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> cudnn_batch_norm_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) {
    return at::_ops::cudnn_batch_norm_out::call(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, out0, out1, out2, out3);
}

// aten::cudnn_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> cudnn_batch_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon, const at::Tensor & reserveSpace) {
    return at::_ops::cudnn_batch_norm_backward_out::call(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace, out0, out1, out2);
}
// aten::cudnn_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> cudnn_batch_norm_backward_outf(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon, const at::Tensor & reserveSpace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::cudnn_batch_norm_backward_out::call(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace, out0, out1, out2);
}

// aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_transpose_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & cudnn_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_transpose_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out);
  }
}

// aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) {
    return at::_ops::cudnn_convolution_transpose_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & cudnn_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) {
    return at::_ops::cudnn_convolution_transpose_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out);
  }
}

// aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_transpose_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_transpose_out::call(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & cudnn_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) {
    return at::_ops::cudnn_convolution_transpose_out::call(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out);
  }
}

// aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_transpose_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) {
    return at::_ops::cudnn_convolution_transpose_out::call(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & cudnn_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) {
    return at::_ops::cudnn_convolution_transpose_out::call(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out);
  }
}

// aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mps_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_mps_convolution_transpose_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _mps_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_mps_convolution_transpose_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mps_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::_mps_convolution_transpose_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _mps_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::_mps_convolution_transpose_out::call(self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mps_convolution_transpose_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_mps_convolution_transpose_out::call(self, weight, padding, output_padding, stride, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _mps_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_mps_convolution_transpose_out::call(self, weight, padding, output_padding, stride, dilation, groups, out);
  }
}

// aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mps_convolution_transpose_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::_mps_convolution_transpose_out::call(self, weight, padding, output_padding, stride, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _mps_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::_mps_convolution_transpose_out::call(self, weight, padding, output_padding, stride, dilation, groups, out);
  }
}

// aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,2> output_mask) {
    return at::_ops::mps_convolution_transpose_backward_out::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,2> output_mask) {
    return at::_ops::mps_convolution_transpose_backward_out::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1);
  }
}

// aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::mps_convolution_transpose_backward_out::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::mps_convolution_transpose_backward_out::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1);
  }
}

// aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_symint_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,2> output_mask) {
    return at::_ops::mps_convolution_transpose_backward_out::call(self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask, out0, out1);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,2> output_mask) {
    return at::_ops::mps_convolution_transpose_backward_out::call(self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask, out0, out1);
  }
}

// aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_symint_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::mps_convolution_transpose_backward_out::call(self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask, out0, out1);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &> mps_convolution_transpose_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::mps_convolution_transpose_backward_out::call(self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask, out0, out1);
  }
}

// aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::cudnn_convolution_relu_out::call(self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & cudnn_convolution_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::cudnn_convolution_relu_out::call(self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_relu_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::cudnn_convolution_relu_out::call(self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & cudnn_convolution_relu_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::cudnn_convolution_relu_out::call(self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_relu_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::cudnn_convolution_relu_out::call(self, weight, bias, stride, padding, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & cudnn_convolution_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::cudnn_convolution_relu_out::call(self, weight, bias, stride, padding, dilation, groups, out);
  }
}

// aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_relu_symint_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::cudnn_convolution_relu_out::call(self, weight, bias, stride, padding, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & cudnn_convolution_relu_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::cudnn_convolution_relu_out::call(self, weight, bias, stride, padding, dilation, groups, out);
  }
}

// aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::cudnn_convolution_add_relu_out::call(self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & cudnn_convolution_add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::cudnn_convolution_add_relu_out::call(self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_add_relu_outf(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::cudnn_convolution_add_relu_out::call(self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & cudnn_convolution_add_relu_outf(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::cudnn_convolution_add_relu_out::call(self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_add_relu_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::cudnn_convolution_add_relu_out::call(self, weight, z, alpha, bias, stride, padding, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & cudnn_convolution_add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::cudnn_convolution_add_relu_out::call(self, weight, z, alpha, bias, stride, padding, dilation, groups, out);
  }
}

// aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_convolution_add_relu_symint_outf(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::cudnn_convolution_add_relu_out::call(self, weight, z, alpha, bias, stride, padding, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & cudnn_convolution_add_relu_outf(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const ::std::optional<at::Scalar> & alpha, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::cudnn_convolution_add_relu_out::call(self, weight, z, alpha, bias, stride, padding, dilation, groups, out);
  }
}

// aten::cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_grid_sampler_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & grid) {
    return at::_ops::cudnn_grid_sampler_out::call(self, grid, out);
}
// aten::cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cudnn_grid_sampler_outf(const at::Tensor & self, const at::Tensor & grid, at::Tensor & out) {
    return at::_ops::cudnn_grid_sampler_out::call(self, grid, out);
}

// aten::cudnn_grid_sampler_backward.out(Tensor self, Tensor grid, Tensor grad_output, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> cudnn_grid_sampler_backward_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output) {
    return at::_ops::cudnn_grid_sampler_backward_out::call(self, grid, grad_output, out0, out1);
}
// aten::cudnn_grid_sampler_backward.out(Tensor self, Tensor grid, Tensor grad_output, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> cudnn_grid_sampler_backward_outf(const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::cudnn_grid_sampler_backward_out::call(self, grid, grad_output, out0, out1);
}

// aten::_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _ctc_loss_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false) {
    return at::_ops::_ctc_loss_out::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1);
}
// aten::_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _ctc_loss_outf(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_ctc_loss_out::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1);
}

// aten::_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _ctc_loss_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank=0, bool zero_infinity=false) {
    return at::_ops::_ctc_loss_Tensor_out::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1);
}
// aten::_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _ctc_loss_outf(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_ctc_loss_Tensor_out::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1);
}

// aten::_ctc_loss_backward.out(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _ctc_loss_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity=false) {
    return at::_ops::_ctc_loss_backward_out::call(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity, out);
}
// aten::_ctc_loss_backward.out(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _ctc_loss_backward_outf(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity, at::Tensor & out) {
    return at::_ops::_ctc_loss_backward_out::call(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity, out);
}

// aten::diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diag_embed_out(at::Tensor & out, const at::Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1) {
    return at::_ops::diag_embed_out::call(self, offset, dim1, dim2, out);
}
// aten::diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diag_embed_outf(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) {
    return at::_ops::diag_embed_out::call(self, offset, dim1, dim2, out);
}

// aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diagonal_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) {
    return at::_ops::diagonal_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & diagonal_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) {
    return at::_ops::diagonal_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2, out);
  }
}

// aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diagonal_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) {
    return at::_ops::diagonal_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & diagonal_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) {
    return at::_ops::diagonal_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2, out);
  }
}

// aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diagonal_backward_symint_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) {
    return at::_ops::diagonal_backward_out::call(grad_output, input_sizes, offset, dim1, dim2, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & diagonal_backward_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) {
    return at::_ops::diagonal_backward_out::call(grad_output, input_sizes, offset, dim1, dim2, out);
  }
}

// aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diagonal_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) {
    return at::_ops::diagonal_backward_out::call(grad_output, input_sizes, offset, dim1, dim2, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & diagonal_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) {
    return at::_ops::diagonal_backward_out::call(grad_output, input_sizes, offset, dim1, dim2, out);
  }
}

// aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & div_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::div_Scalar_out::call(self, other, out);
}
// aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & div_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::div_Scalar_out::call(self, other, out);
}

// aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & div_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode) {
    return at::_ops::div_Scalar_mode_out::call(self, other, rounding_mode, out);
}
// aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & div_outf(const at::Tensor & self, const at::Scalar & other, ::std::optional<c10::string_view> rounding_mode, at::Tensor & out) {
    return at::_ops::div_Scalar_mode_out::call(self, other, rounding_mode, out);
}

// aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_out(at::Tensor & out, const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) {
    return at::_ops::embedding_out::call(weight, indices, padding_idx, scale_grad_by_freq, sparse, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & embedding_out(at::Tensor & out, const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) {
    return at::_ops::embedding_out::call(weight, indices, padding_idx, scale_grad_by_freq, sparse, out);
  }
}

// aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_outf(const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out) {
    return at::_ops::embedding_out::call(weight, indices, padding_idx, scale_grad_by_freq, sparse, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & embedding_outf(const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out) {
    return at::_ops::embedding_out::call(weight, indices, padding_idx, scale_grad_by_freq, sparse, out);
  }
}

// aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_symint_out(at::Tensor & out, const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) {
    return at::_ops::embedding_out::call(weight, indices, padding_idx, scale_grad_by_freq, sparse, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & embedding_out(at::Tensor & out, const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) {
    return at::_ops::embedding_out::call(weight, indices, padding_idx, scale_grad_by_freq, sparse, out);
  }
}

// aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_symint_outf(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out) {
    return at::_ops::embedding_out::call(weight, indices, padding_idx, scale_grad_by_freq, sparse, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & embedding_outf(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out) {
    return at::_ops::embedding_out::call(weight, indices, padding_idx, scale_grad_by_freq, sparse, out);
  }
}

// aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_dense_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_dense_backward_out::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & embedding_dense_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_dense_backward_out::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out);
  }
}

// aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_dense_backward_outf(const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, at::Tensor & out) {
    return at::_ops::embedding_dense_backward_out::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & embedding_dense_backward_outf(const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, at::Tensor & out) {
    return at::_ops::embedding_dense_backward_out::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out);
  }
}

// aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_dense_backward_symint_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_dense_backward_out::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & embedding_dense_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) {
    return at::_ops::embedding_dense_backward_out::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out);
  }
}

// aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_dense_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, at::Tensor & out) {
    return at::_ops::embedding_dense_backward_out::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & embedding_dense_backward_outf(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, at::Tensor & out) {
    return at::_ops::embedding_dense_backward_out::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out);
  }
}

// aten::embedding_renorm.out(Tensor self, Tensor indices, float max_norm, float norm_type, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_renorm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) {
    return at::_ops::embedding_renorm_out::call(self, indices, max_norm, norm_type, out);
}
// aten::embedding_renorm.out(Tensor self, Tensor indices, float max_norm, float norm_type, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & embedding_renorm_outf(const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type, at::Tensor & out) {
    return at::_ops::embedding_renorm_out::call(self, indices, max_norm, norm_type, out);
}

// aten::embedding_renorm(Tensor self, Tensor indices, float max_norm, float norm_type) -> Tensor
inline at::Tensor embedding_renorm(const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) {
    return at::_ops::embedding_renorm::call(self, indices, max_norm, norm_type);
}

// aten::_embedding_bag_forward_only.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_forward_only_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const ::std::optional<at::Tensor> & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_forward_only_out::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3);
}
// aten::_embedding_bag_forward_only.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_forward_only_outf(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) {
    return at::_ops::_embedding_bag_forward_only_out::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3);
}

// aten::_embedding_bag.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const ::std::optional<at::Tensor> & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_out::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3);
}
// aten::_embedding_bag.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _embedding_bag_outf(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const ::std::optional<at::Tensor> & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) {
    return at::_ops::_embedding_bag_out::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3);
}

// aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _embedding_bag_dense_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_dense_backward_out::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _embedding_bag_dense_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_dense_backward_out::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out);
  }
}

// aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _embedding_bag_dense_backward_outf(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx, at::Tensor & out) {
    return at::_ops::_embedding_bag_dense_backward_out::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _embedding_bag_dense_backward_outf(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx, at::Tensor & out) {
    return at::_ops::_embedding_bag_dense_backward_out::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out);
  }
}

// aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _embedding_bag_dense_backward_symint_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_dense_backward_out::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _embedding_bag_dense_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_dense_backward_out::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out);
  }
}

// aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _embedding_bag_dense_backward_symint_outf(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx, at::Tensor & out) {
    return at::_ops::_embedding_bag_dense_backward_out::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _embedding_bag_dense_backward_outf(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const ::std::optional<at::Tensor> & per_sample_weights, int64_t padding_idx, at::Tensor & out) {
    return at::_ops::_embedding_bag_dense_backward_out::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out);
  }
}

// aten::_embedding_bag_per_sample_weights_backward.out(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _embedding_bag_per_sample_weights_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx=-1) {
    return at::_ops::_embedding_bag_per_sample_weights_backward_out::call(grad, weight, indices, offsets, offset2bag, mode, padding_idx, out);
}
// aten::_embedding_bag_per_sample_weights_backward.out(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _embedding_bag_per_sample_weights_backward_outf(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx, at::Tensor & out) {
    return at::_ops::_embedding_bag_per_sample_weights_backward_out::call(grad, weight, indices, offsets, offset2bag, mode, padding_idx, out);
}

// aten::empty.names_out(int[] size, *, Dimname[]? names, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_names_out::call(size, names, memory_format, out);
}
// aten::empty.names_out(int[] size, *, Dimname[]? names, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::empty_names_out::call(size, names, memory_format, out);
}

// aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_permuted_out(at::Tensor & out, at::IntArrayRef size, at::IntArrayRef physical_layout) {
    return at::_ops::empty_permuted_out::call(c10::fromIntArrayRefSlow(size), physical_layout, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & empty_permuted_out(at::Tensor & out, at::IntArrayRef size, at::IntArrayRef physical_layout) {
    return at::_ops::empty_permuted_out::call(c10::fromIntArrayRefSlow(size), physical_layout, out);
  }
}

// aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_permuted_outf(at::IntArrayRef size, at::IntArrayRef physical_layout, at::Tensor & out) {
    return at::_ops::empty_permuted_out::call(c10::fromIntArrayRefSlow(size), physical_layout, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & empty_permuted_outf(at::IntArrayRef size, at::IntArrayRef physical_layout, at::Tensor & out) {
    return at::_ops::empty_permuted_out::call(c10::fromIntArrayRefSlow(size), physical_layout, out);
  }
}

// aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_permuted_symint_out(at::Tensor & out, c10::SymIntArrayRef size, at::IntArrayRef physical_layout) {
    return at::_ops::empty_permuted_out::call(size, physical_layout, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & empty_permuted_out(at::Tensor & out, c10::SymIntArrayRef size, at::IntArrayRef physical_layout) {
    return at::_ops::empty_permuted_out::call(size, physical_layout, out);
  }
}

// aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_permuted_symint_outf(c10::SymIntArrayRef size, at::IntArrayRef physical_layout, at::Tensor & out) {
    return at::_ops::empty_permuted_out::call(size, physical_layout, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & empty_permuted_outf(c10::SymIntArrayRef size, at::IntArrayRef physical_layout, at::Tensor & out) {
    return at::_ops::empty_permuted_out::call(size, physical_layout, out);
  }
}

// aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_empty_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::new_empty_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_empty_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::new_empty_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_empty_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::new_empty_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_empty_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::new_empty_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_empty_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::new_empty_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_empty_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::new_empty_out::call(self, size, out);
  }
}

// aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_empty_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::new_empty_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_empty_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::new_empty_out::call(self, size, out);
  }
}

// aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_empty_strided_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::new_empty_strided_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_empty_strided_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::new_empty_strided_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_empty_strided_outf(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::new_empty_strided_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_empty_strided_outf(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::new_empty_strided_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_empty_strided_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::new_empty_strided_out::call(self, size, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_empty_strided_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::new_empty_strided_out::call(self, size, stride, out);
  }
}

// aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_empty_strided_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::new_empty_strided_out::call(self, size, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_empty_strided_outf(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::new_empty_strided_out::call(self, size, stride, out);
  }
}

// aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_full_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value) {
    return at::_ops::new_full_out::call(self, c10::fromIntArrayRefSlow(size), fill_value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_full_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value) {
    return at::_ops::new_full_out::call(self, c10::fromIntArrayRefSlow(size), fill_value, out);
  }
}

// aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_full_outf(const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) {
    return at::_ops::new_full_out::call(self, c10::fromIntArrayRefSlow(size), fill_value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_full_outf(const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) {
    return at::_ops::new_full_out::call(self, c10::fromIntArrayRefSlow(size), fill_value, out);
  }
}

// aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_full_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value) {
    return at::_ops::new_full_out::call(self, size, fill_value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_full_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value) {
    return at::_ops::new_full_out::call(self, size, fill_value, out);
  }
}

// aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_full_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) {
    return at::_ops::new_full_out::call(self, size, fill_value, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_full_outf(const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) {
    return at::_ops::new_full_out::call(self, size, fill_value, out);
  }
}

// aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_zeros_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::new_zeros_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_zeros_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::new_zeros_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_zeros_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::new_zeros_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_zeros_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::new_zeros_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_zeros_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::new_zeros_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_zeros_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::new_zeros_out::call(self, size, out);
  }
}

// aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_zeros_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::new_zeros_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_zeros_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::new_zeros_out::call(self, size, out);
  }
}

// aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_ones_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::new_ones_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_ones_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::new_ones_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_ones_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::new_ones_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & new_ones_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::new_ones_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_ones_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::new_ones_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_ones_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::new_ones_out::call(self, size, out);
  }
}

// aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & new_ones_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::new_ones_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & new_ones_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::new_ones_out::call(self, size, out);
  }
}

// aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _empty_affine_quantized_out(at::Tensor & out, at::IntArrayRef size, double scale=1, int64_t zero_point=0, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _empty_affine_quantized_out(at::Tensor & out, at::IntArrayRef size, double scale=1, int64_t zero_point=0, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out);
  }
}

// aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _empty_affine_quantized_outf(at::IntArrayRef size, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_empty_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _empty_affine_quantized_outf(at::IntArrayRef size, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_empty_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out);
  }
}

// aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _empty_affine_quantized_symint_out(at::Tensor & out, c10::SymIntArrayRef size, double scale=1, int64_t zero_point=0, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _empty_affine_quantized_out(at::Tensor & out, c10::SymIntArrayRef size, double scale=1, int64_t zero_point=0, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out);
  }
}

// aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _empty_affine_quantized_symint_outf(c10::SymIntArrayRef size, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _empty_affine_quantized_outf(c10::SymIntArrayRef size, double scale, int64_t zero_point, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out);
  }
}

// aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _empty_per_channel_affine_quantized_out(at::Tensor & out, at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_per_channel_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scales, zero_points, axis, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _empty_per_channel_affine_quantized_out(at::Tensor & out, at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_per_channel_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scales, zero_points, axis, memory_format, out);
  }
}

// aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _empty_per_channel_affine_quantized_outf(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_empty_per_channel_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scales, zero_points, axis, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _empty_per_channel_affine_quantized_outf(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_empty_per_channel_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scales, zero_points, axis, memory_format, out);
  }
}

// aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _empty_per_channel_affine_quantized_symint_out(at::Tensor & out, c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_per_channel_affine_quantized_out::call(size, scales, zero_points, axis, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _empty_per_channel_affine_quantized_out(at::Tensor & out, c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format=c10::MemoryFormat::Contiguous) {
    return at::_ops::_empty_per_channel_affine_quantized_out::call(size, scales, zero_points, axis, memory_format, out);
  }
}

// aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _empty_per_channel_affine_quantized_symint_outf(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_empty_per_channel_affine_quantized_out::call(size, scales, zero_points, axis, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _empty_per_channel_affine_quantized_outf(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_empty_per_channel_affine_quantized_out::call(size, scales, zero_points, axis, memory_format, out);
  }
}

// aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & resize_out(const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_out::call(self, c10::fromIntArrayRefSlow(size), memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & resize_out(const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_out::call(self, c10::fromIntArrayRefSlow(size), memory_format, out);
  }
}

// aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & resize_outf(const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, const at::Tensor & out) {
    return at::_ops::resize_out::call(self, c10::fromIntArrayRefSlow(size), memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & resize_outf(const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, const at::Tensor & out) {
    return at::_ops::resize_out::call(self, c10::fromIntArrayRefSlow(size), memory_format, out);
  }
}

// aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & resize_symint_out(const at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_out::call(self, size, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & resize_out(const at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_out::call(self, size, memory_format, out);
  }
}

// aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & resize_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, const at::Tensor & out) {
    return at::_ops::resize_out::call(self, size, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & resize_outf(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format, const at::Tensor & out) {
    return at::_ops::resize_out::call(self, size, memory_format, out);
  }
}

// aten::resize(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor resize(const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize::call(self, c10::fromIntArrayRefSlow(size), memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor resize(const at::Tensor & self, at::IntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize::call(self, c10::fromIntArrayRefSlow(size), memory_format);
  }
}

// aten::resize(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor resize_symint(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize::call(self, size, memory_format);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor resize(const at::Tensor & self, c10::SymIntArrayRef size, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize::call(self, size, memory_format);
  }
}

// aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & _resize_output_out(const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::Device device) {
    return at::_ops::_resize_output_out::call(self, c10::fromIntArrayRefSlow(size), device, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & _resize_output_out(const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::Device device) {
    return at::_ops::_resize_output_out::call(self, c10::fromIntArrayRefSlow(size), device, out);
  }
}

// aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & _resize_output_outf(const at::Tensor & self, at::IntArrayRef size, at::Device device, const at::Tensor & out) {
    return at::_ops::_resize_output_out::call(self, c10::fromIntArrayRefSlow(size), device, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  const at::Tensor & _resize_output_outf(const at::Tensor & self, at::IntArrayRef size, at::Device device, const at::Tensor & out) {
    return at::_ops::_resize_output_out::call(self, c10::fromIntArrayRefSlow(size), device, out);
  }
}

// aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & _resize_output_symint_out(const at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) {
    return at::_ops::_resize_output_out::call(self, size, device, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & _resize_output_out(const at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) {
    return at::_ops::_resize_output_out::call(self, size, device, out);
  }
}

// aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & _resize_output_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device, const at::Tensor & out) {
    return at::_ops::_resize_output_out::call(self, size, device, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  const at::Tensor & _resize_output_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device, const at::Tensor & out) {
    return at::_ops::_resize_output_out::call(self, size, device, out);
  }
}

// aten::_resize_output(Tensor self, SymInt[] size, Device device) -> Tensor
inline at::Tensor _resize_output(const at::Tensor & self, at::IntArrayRef size, at::Device device) {
    return at::_ops::_resize_output::call(self, c10::fromIntArrayRefSlow(size), device);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor _resize_output(const at::Tensor & self, at::IntArrayRef size, at::Device device) {
    return at::_ops::_resize_output::call(self, c10::fromIntArrayRefSlow(size), device);
  }
}

// aten::_resize_output(Tensor self, SymInt[] size, Device device) -> Tensor
inline at::Tensor _resize_output_symint(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) {
    return at::_ops::_resize_output::call(self, size, device);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor _resize_output(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) {
    return at::_ops::_resize_output::call(self, size, device);
  }
}

// aten::empty_quantized.out(int[] size, Tensor qtensor, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_quantized_out(at::Tensor & out, at::IntArrayRef size, const at::Tensor & qtensor, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_quantized_out::call(size, qtensor, memory_format, out);
}
// aten::empty_quantized.out(int[] size, Tensor qtensor, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_quantized_outf(at::IntArrayRef size, const at::Tensor & qtensor, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::empty_quantized_out::call(size, qtensor, memory_format, out);
}

// aten::empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::empty_like_out::call(self, memory_format, out);
}
// aten::empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::empty_like_out::call(self, memory_format, out);
}

// aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_strided_out(at::Tensor & out, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::empty_strided_out::call(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & empty_strided_out(at::Tensor & out, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::empty_strided_out::call(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_strided_outf(at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::empty_strided_out::call(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & empty_strided_outf(at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::empty_strided_out::call(c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_strided_symint_out(at::Tensor & out, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::empty_strided_out::call(size, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & empty_strided_out(at::Tensor & out, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::empty_strided_out::call(size, stride, out);
  }
}

// aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & empty_strided_symint_outf(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::empty_strided_out::call(size, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & empty_strided_outf(c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::empty_strided_out::call(size, stride, out);
  }
}

// aten::fill.Scalar_out(Tensor self, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fill_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & value) {
    return at::_ops::fill_Scalar_out::call(self, value, out);
}
// aten::fill.Scalar_out(Tensor self, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fill_outf(const at::Tensor & self, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::fill_Scalar_out::call(self, value, out);
}

// aten::fill.Tensor_out(Tensor self, Tensor value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fill_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & value) {
    return at::_ops::fill_Tensor_out::call(self, value, out);
}
// aten::fill.Tensor_out(Tensor self, Tensor value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & fill_outf(const at::Tensor & self, const at::Tensor & value, at::Tensor & out) {
    return at::_ops::fill_Tensor_out::call(self, value, out);
}

// aten::floor_divide.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & floor_divide_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::floor_divide_Scalar_out::call(self, other, out);
}
// aten::floor_divide.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & floor_divide_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::floor_divide_Scalar_out::call(self, other, out);
}

// aten::full.names_out(int[] size, Scalar fill_value, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & full_out(at::Tensor & out, at::IntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::DimnameList> names) {
    return at::_ops::full_names_out::call(size, fill_value, names, out);
}
// aten::full.names_out(int[] size, Scalar fill_value, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & full_outf(at::IntArrayRef size, const at::Scalar & fill_value, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::full_names_out::call(size, fill_value, names, out);
}

// aten::full_like.out(Tensor self, Scalar fill_value, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & full_like_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & fill_value, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::full_like_out::call(self, fill_value, memory_format, out);
}
// aten::full_like.out(Tensor self, Scalar fill_value, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & full_like_outf(const at::Tensor & self, const at::Scalar & fill_value, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::full_like_out::call(self, fill_value, memory_format, out);
}

// aten::from_file.out(str filename, bool? shared=None, int? size=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & from_file_out(at::Tensor & out, c10::string_view filename, ::std::optional<bool> shared=::std::nullopt, ::std::optional<int64_t> size=0) {
    return at::_ops::from_file_out::call(filename, shared, size, out);
}
// aten::from_file.out(str filename, bool? shared=None, int? size=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & from_file_outf(c10::string_view filename, ::std::optional<bool> shared, ::std::optional<int64_t> size, at::Tensor & out) {
    return at::_ops::from_file_out::call(filename, shared, size, out);
}

// aten::grid_sampler_2d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & grid_sampler_2d_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
    return at::_ops::grid_sampler_2d_out::call(input, grid, interpolation_mode, padding_mode, align_corners, out);
}
// aten::grid_sampler_2d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & grid_sampler_2d_outf(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) {
    return at::_ops::grid_sampler_2d_out::call(input, grid, interpolation_mode, padding_mode, align_corners, out);
}

// aten::grid_sampler_2d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_2d_backward_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) {
    return at::_ops::grid_sampler_2d_backward_out::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1);
}
// aten::grid_sampler_2d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::grid_sampler_2d_backward_out::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1);
}

// aten::_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _grid_sampler_2d_cpu_fallback_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
    return at::_ops::_grid_sampler_2d_cpu_fallback_out::call(input, grid, interpolation_mode, padding_mode, align_corners, out);
}
// aten::_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _grid_sampler_2d_cpu_fallback_outf(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) {
    return at::_ops::_grid_sampler_2d_cpu_fallback_out::call(input, grid, interpolation_mode, padding_mode, align_corners, out);
}

// aten::grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & grid_sampler_3d_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
    return at::_ops::grid_sampler_3d_out::call(input, grid, interpolation_mode, padding_mode, align_corners, out);
}
// aten::grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & grid_sampler_3d_outf(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) {
    return at::_ops::grid_sampler_3d_out::call(input, grid, interpolation_mode, padding_mode, align_corners, out);
}

// aten::grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_3d_backward_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask) {
    return at::_ops::grid_sampler_3d_backward_out::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1);
}
// aten::grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::grid_sampler_3d_backward_out::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1);
}

// aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hann_window_out(at::Tensor & out, int64_t window_length) {
    return at::_ops::hann_window_out::call(window_length, out);
}
// aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hann_window_outf(int64_t window_length, at::Tensor & out) {
    return at::_ops::hann_window_out::call(window_length, out);
}

// aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hann_window_out(at::Tensor & out, int64_t window_length, bool periodic) {
    return at::_ops::hann_window_periodic_out::call(window_length, periodic, out);
}
// aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hann_window_outf(int64_t window_length, bool periodic, at::Tensor & out) {
    return at::_ops::hann_window_periodic_out::call(window_length, periodic, out);
}

// aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hamming_window_out(at::Tensor & out, int64_t window_length) {
    return at::_ops::hamming_window_out::call(window_length, out);
}
// aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hamming_window_outf(int64_t window_length, at::Tensor & out) {
    return at::_ops::hamming_window_out::call(window_length, out);
}

// aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hamming_window_out(at::Tensor & out, int64_t window_length, bool periodic) {
    return at::_ops::hamming_window_periodic_out::call(window_length, periodic, out);
}
// aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hamming_window_outf(int64_t window_length, bool periodic, at::Tensor & out) {
    return at::_ops::hamming_window_periodic_out::call(window_length, periodic, out);
}

// aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hamming_window_out(at::Tensor & out, int64_t window_length, bool periodic, double alpha) {
    return at::_ops::hamming_window_periodic_alpha_out::call(window_length, periodic, alpha, out);
}
// aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hamming_window_outf(int64_t window_length, bool periodic, double alpha, at::Tensor & out) {
    return at::_ops::hamming_window_periodic_alpha_out::call(window_length, periodic, alpha, out);
}

// aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hamming_window_out(at::Tensor & out, int64_t window_length, bool periodic, double alpha, double beta) {
    return at::_ops::hamming_window_periodic_alpha_beta_out::call(window_length, periodic, alpha, beta, out);
}
// aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hamming_window_outf(int64_t window_length, bool periodic, double alpha, double beta, at::Tensor & out) {
    return at::_ops::hamming_window_periodic_alpha_beta_out::call(window_length, periodic, alpha, beta, out);
}

// aten::kaiser_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & kaiser_window_out(at::Tensor & out, int64_t window_length) {
    return at::_ops::kaiser_window_out::call(window_length, out);
}
// aten::kaiser_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & kaiser_window_outf(int64_t window_length, at::Tensor & out) {
    return at::_ops::kaiser_window_out::call(window_length, out);
}

// aten::kaiser_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & kaiser_window_out(at::Tensor & out, int64_t window_length, bool periodic) {
    return at::_ops::kaiser_window_periodic_out::call(window_length, periodic, out);
}
// aten::kaiser_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & kaiser_window_outf(int64_t window_length, bool periodic, at::Tensor & out) {
    return at::_ops::kaiser_window_periodic_out::call(window_length, periodic, out);
}

// aten::kaiser_window.beta_out(int window_length, bool periodic, float beta, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & kaiser_window_out(at::Tensor & out, int64_t window_length, bool periodic, double beta) {
    return at::_ops::kaiser_window_beta_out::call(window_length, periodic, beta, out);
}
// aten::kaiser_window.beta_out(int window_length, bool periodic, float beta, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & kaiser_window_outf(int64_t window_length, bool periodic, double beta, at::Tensor & out) {
    return at::_ops::kaiser_window_beta_out::call(window_length, periodic, beta, out);
}

// aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps) {
    return at::_ops::native_group_norm_out::call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps) {
    return at::_ops::native_group_norm_out::call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2);
  }
}

// aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_group_norm_out::call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_group_norm_out::call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2);
  }
}

// aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) {
    return at::_ops::native_group_norm_out::call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) {
    return at::_ops::native_group_norm_out::call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2);
  }
}

// aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_symint_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_group_norm_out::call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_group_norm_out::call(input, weight, bias, N, C, HxW, group, eps, out0, out1, out2);
  }
}

// aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array<bool,3> output_mask) {
    return at::_ops::native_group_norm_backward_out::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array<bool,3> output_mask) {
    return at::_ops::native_group_norm_backward_out::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2);
  }
}

// aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_outf(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_group_norm_backward_out::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_outf(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_group_norm_backward_out::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2);
  }
}

// aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask) {
    return at::_ops::native_group_norm_backward_out::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask) {
    return at::_ops::native_group_norm_backward_out::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2);
  }
}

// aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_symint_outf(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_group_norm_backward_out::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_group_norm_backward_outf(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_group_norm_backward_out::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2);
  }
}

// aten::index_put.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_put_out(at::Tensor & out, const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false) {
    return at::_ops::index_put_out::call(self, indices, values, accumulate, out);
}
// aten::index_put.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_put_outf(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, at::Tensor & out) {
    return at::_ops::index_put_out::call(self, indices, values, accumulate, out);
}

// aten::_index_put_impl.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _index_put_impl_out(at::Tensor & out, const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false, bool unsafe=false) {
    return at::_ops::_index_put_impl_out::call(self, indices, values, accumulate, unsafe, out);
}
// aten::_index_put_impl.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _index_put_impl_outf(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, bool unsafe, at::Tensor & out) {
    return at::_ops::_index_put_impl_out::call(self, indices, values, accumulate, unsafe, out);
}

// aten::_index_put_impl(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor
inline at::Tensor _index_put_impl(const at::Tensor & self, const c10::List<::std::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false, bool unsafe=false) {
    return at::_ops::_index_put_impl::call(self, indices, values, accumulate, unsafe);
}

// aten::isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isnan_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::isnan_out::call(self, out);
}
// aten::isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isnan_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::isnan_out::call(self, out);
}

// aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps) {
    return at::_ops::native_layer_norm_out::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps) {
    return at::_ops::native_layer_norm_out::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, out0, out1, out2);
  }
}

// aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_outf(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_layer_norm_out::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_outf(const at::Tensor & input, at::IntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_layer_norm_out::call(input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, out0, out1, out2);
  }
}

// aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps) {
    return at::_ops::native_layer_norm_out::call(input, normalized_shape, weight, bias, eps, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps) {
    return at::_ops::native_layer_norm_out::call(input, normalized_shape, weight, bias, eps, out0, out1, out2);
  }
}

// aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_symint_outf(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_layer_norm_out::call(input, normalized_shape, weight, bias, eps, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_outf(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_layer_norm_out::call(input, normalized_shape, weight, bias, eps, out0, out1, out2);
  }
}

// aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) {
    return at::_ops::native_layer_norm_backward_out::call(grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) {
    return at::_ops::native_layer_norm_backward_out::call(grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask, out0, out1, out2);
  }
}

// aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_outf(const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_layer_norm_backward_out::call(grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_outf(const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_layer_norm_backward_out::call(grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask, out0, out1, out2);
  }
}

// aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) {
    return at::_ops::native_layer_norm_backward_out::call(grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask) {
    return at::_ops::native_layer_norm_backward_out::call(grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask, out0, out1, out2);
  }
}

// aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_symint_outf(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_layer_norm_backward_out::call(grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_layer_norm_backward_outf(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_layer_norm_backward_out::call(grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask, out0, out1, out2);
  }
}

// aten::linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linear_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask) {
    return at::_ops::linear_backward_out::call(self, grad_output, weight, output_mask, out0, out1, out2);
}
// aten::linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> linear_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::linear_backward_out::call(self, grad_output, weight, output_mask, out0, out1, out2);
}

// aten::mkldnn_linear.out(Tensor self, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_linear_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias={}) {
    return at::_ops::mkldnn_linear_out::call(self, weight, bias, out);
}
// aten::mkldnn_linear.out(Tensor self, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_linear_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & out) {
    return at::_ops::mkldnn_linear_out::call(self, weight, bias, out);
}

// aten::mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_linear_backward_input_out(at::Tensor & out, at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight) {
    return at::_ops::mkldnn_linear_backward_input_out::call(input_size, grad_output, weight, out);
}
// aten::mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_linear_backward_input_outf(at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight, at::Tensor & out) {
    return at::_ops::mkldnn_linear_backward_input_out::call(input_size, grad_output, weight, out);
}

// aten::mkldnn_linear_backward_weights.out(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> mkldnn_linear_backward_weights_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined) {
    return at::_ops::mkldnn_linear_backward_weights_out::call(grad_output, input, weight, bias_defined, out0, out1);
}
// aten::mkldnn_linear_backward_weights.out(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> mkldnn_linear_backward_weights_outf(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::mkldnn_linear_backward_weights_out::call(grad_output, input, weight, bias_defined, out0, out1);
}

// aten::mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_linear_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask) {
    return at::_ops::mkldnn_linear_backward_out::call(self, grad_output, weight, output_mask, out0, out1, out2);
}
// aten::mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_linear_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::mkldnn_linear_backward_out::call(self, grad_output, weight, output_mask, out0, out1, out2);
}

// aten::matmul_backward.out(Tensor grad, Tensor self, Tensor other, bool[2] mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> matmul_backward_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array<bool,2> mask) {
    return at::_ops::matmul_backward_out::call(grad, self, other, mask, out0, out1);
}
// aten::matmul_backward.out(Tensor grad, Tensor self, Tensor other, bool[2] mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> matmul_backward_outf(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array<bool,2> mask, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::matmul_backward_out::call(grad, self, other, mask, out0, out1);
}

// aten::_aminmax.out(Tensor self, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _aminmax_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self) {
    return at::_ops::_aminmax_out::call(self, out0, out1);
}
// aten::_aminmax.out(Tensor self, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _aminmax_outf(const at::Tensor & self, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_aminmax_out::call(self, out0, out1);
}

// aten::_aminmax.dim_out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _aminmax_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, int64_t dim, bool keepdim=false) {
    return at::_ops::_aminmax_dim_out::call(self, dim, keepdim, out0, out1);
}
// aten::_aminmax.dim_out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _aminmax_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_aminmax_dim_out::call(self, dim, keepdim, out0, out1);
}

// aten::max_pool2d_backward.out(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_pool2d_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::max_pool2d_backward_out::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, out);
}
// aten::max_pool2d_backward.out(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & max_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) {
    return at::_ops::max_pool2d_backward_out::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, out);
}

// aten::mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_max_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::mkldnn_max_pool2d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}
// aten::mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_max_pool2d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) {
    return at::_ops::mkldnn_max_pool2d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}

// aten::mkldnn_max_pool2d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_max_pool2d_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::mkldnn_max_pool2d_backward_out::call(grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode, out);
}
// aten::mkldnn_max_pool2d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_max_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) {
    return at::_ops::mkldnn_max_pool2d_backward_out::call(grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode, out);
}

// aten::mkldnn_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_max_pool3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::mkldnn_max_pool3d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}
// aten::mkldnn_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_max_pool3d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) {
    return at::_ops::mkldnn_max_pool3d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}

// aten::mkldnn_max_pool3d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_max_pool3d_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::mkldnn_max_pool3d_backward_out::call(grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode, out);
}
// aten::mkldnn_max_pool3d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_max_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) {
    return at::_ops::mkldnn_max_pool3d_backward_out::call(grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode, out);
}

// aten::quantized_max_pool1d.out(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantized_max_pool1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::quantized_max_pool1d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}
// aten::quantized_max_pool1d.out(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantized_max_pool1d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) {
    return at::_ops::quantized_max_pool1d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}

// aten::quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantized_max_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::quantized_max_pool2d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}
// aten::quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantized_max_pool2d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) {
    return at::_ops::quantized_max_pool2d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}

// aten::quantized_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantized_max_pool3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) {
    return at::_ops::quantized_max_pool3d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}
// aten::quantized_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantized_max_pool3d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) {
    return at::_ops::quantized_max_pool3d_out::call(self, kernel_size, stride, padding, dilation, ceil_mode, out);
}

// aten::median.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & median_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::median_out::call(self, out);
}
// aten::median.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & median_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::median_out::call(self, out);
}

// aten::nanmedian.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nanmedian_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::nanmedian_out::call(self, out);
}
// aten::nanmedian.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & nanmedian_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::nanmedian_out::call(self, out);
}

// aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mps_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_mps_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _mps_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::_mps_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mps_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::_mps_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _mps_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::_mps_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mps_convolution_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_mps_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _mps_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::_mps_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, out);
  }
}

// aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mps_convolution_symint_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::_mps_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _mps_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::_mps_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, out);
  }
}

// aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::mps_convolution_backward_out::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,3> output_mask) {
    return at::_ops::mps_convolution_backward_out::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1, out2);
  }
}

// aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::mps_convolution_backward_out::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::mps_convolution_backward_out::call(self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1, out2);
  }
}

// aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::mps_convolution_backward_out::call(self, grad_output, weight, padding, stride, dilation, groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask) {
    return at::_ops::mps_convolution_backward_out::call(self, grad_output, weight, padding, stride, dilation, groups, output_mask, out0, out1, out2);
  }
}

// aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_symint_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::mps_convolution_backward_out::call(self, grad_output, weight, padding, stride, dilation, groups, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::mps_convolution_backward_out::call(self, grad_output, weight, padding, stride, dilation, groups, output_mask, out0, out1, out2);
  }
}

// aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::mkldnn_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & mkldnn_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) {
    return at::_ops::mkldnn_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::mkldnn_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & mkldnn_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) {
    return at::_ops::mkldnn_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out);
  }
}

// aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_convolution_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::mkldnn_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & mkldnn_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) {
    return at::_ops::mkldnn_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, out);
  }
}

// aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_convolution_symint_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::mkldnn_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & mkldnn_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::mkldnn_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, out);
  }
}

// aten::mkldnn_rnn_layer.out(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, int[] batch_sizes, int mode, int hidden_size, int num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) {
    return at::_ops::mkldnn_rnn_layer_out::call(input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train, out0, out1, out2, out3);
}
// aten::mkldnn_rnn_layer.out(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, int[] batch_sizes, int mode, int hidden_size, int num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_outf(const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) {
    return at::_ops::mkldnn_rnn_layer_out::call(input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train, out0, out1, out2, out3);
}

// aten::mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6, const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) {
    return at::_ops::mkldnn_rnn_layer_backward_out::call(input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace, out0, out1, out2, out3, out4, out5, out6);
}
// aten::mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> mkldnn_rnn_layer_backward_outf(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6) {
    return at::_ops::mkldnn_rnn_layer_backward_out::call(input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace, out0, out1, out2, out3, out4, out5, out6);
}

// aten::miopen_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> miopen_batch_norm_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon) {
    return at::_ops::miopen_batch_norm_out::call(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, out0, out1, out2);
}
// aten::miopen_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> miopen_batch_norm_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, bool training, double exponential_average_factor, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::miopen_batch_norm_out::call(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, out0, out1, out2);
}

// aten::miopen_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> miopen_batch_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon) {
    return at::_ops::miopen_batch_norm_backward_out::call(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, out0, out1, out2);
}
// aten::miopen_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> miopen_batch_norm_backward_outf(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_var, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::miopen_batch_norm_backward_out::call(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, out0, out1, out2);
}

// aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & miopen_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
  }
}

// aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & miopen_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
  }
}

// aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_convolution_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & miopen_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out);
  }
}

// aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_convolution_symint_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & miopen_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out);
  }
}

// aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_transpose_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & miopen_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_transpose_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
  }
}

// aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_convolution_transpose_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & miopen_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_convolution_transpose_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
  }
}

// aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_convolution_transpose_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_transpose_out::call(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & miopen_convolution_transpose_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_convolution_transpose_out::call(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic, out);
  }
}

// aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_convolution_transpose_symint_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_convolution_transpose_out::call(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & miopen_convolution_transpose_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_convolution_transpose_out::call(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic, out);
  }
}

// aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_depthwise_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_depthwise_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & miopen_depthwise_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_depthwise_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
  }
}

// aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_depthwise_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_depthwise_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & miopen_depthwise_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_depthwise_convolution_out::call(self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out);
  }
}

// aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_depthwise_convolution_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_depthwise_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & miopen_depthwise_convolution_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) {
    return at::_ops::miopen_depthwise_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out);
  }
}

// aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & miopen_depthwise_convolution_symint_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_depthwise_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & miopen_depthwise_convolution_outf(const at::Tensor & self, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) {
    return at::_ops::miopen_depthwise_convolution_out::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out);
  }
}

// aten::miopen_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> miopen_rnn_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state) {
    return at::_ops::miopen_rnn_out::call(input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
}
// aten::miopen_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> miopen_rnn_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
    return at::_ops::miopen_rnn_out::call(input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4);
}

// aten::miopen_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()
inline void miopen_rnn_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask) {
    return at::_ops::miopen_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3);
}
// aten::miopen_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> ()
inline void miopen_rnn_backward_outf(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const ::std::optional<at::Tensor> & cx, const at::Tensor & output, const ::std::optional<at::Tensor> & grad_output, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const ::std::optional<at::Tensor> & dropout_state, const at::Tensor & reserve, ::std::array<bool,4> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) {
    return at::_ops::miopen_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3);
}

// aten::_sparse_sparse_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_sparse_matmul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::_sparse_sparse_matmul_out::call(self, other, out);
}
// aten::_sparse_sparse_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_sparse_matmul_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::_sparse_sparse_matmul_out::call(self, other, out);
}

// aten::mul.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mul_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::mul_Scalar_out::call(self, other, out);
}
// aten::mul.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mul_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::mul_Scalar_out::call(self, other, out);
}

// aten::_native_batch_norm_legit_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _native_batch_norm_legit_functional(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double momentum, double eps) {
    return at::_ops::_native_batch_norm_legit_functional::call(input, weight, bias, running_mean, running_var, training, momentum, eps);
}

// aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_no_training_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) {
    return at::_ops::_native_batch_norm_legit_no_training_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out0, out1, out2);
}
// aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _native_batch_norm_legit_no_training_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_native_batch_norm_legit_no_training_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out0, out1, out2);
}

// aten::batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_stats_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, double eps) {
    return at::_ops::batch_norm_stats_out::call(input, eps, out0, out1);
}
// aten::batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_stats_outf(const at::Tensor & input, double eps, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::batch_norm_stats_out::call(input, eps, out0, out1);
}

// aten::batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count) {
    return at::_ops::batch_norm_gather_stats_out::call(input, mean, invstd, running_mean, running_var, momentum, eps, count, out0, out1);
}
// aten::batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_outf(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, int64_t count, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::batch_norm_gather_stats_out::call(input, mean, invstd, running_mean, running_var, momentum, eps, count, out0, out1);
}

// aten::batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_with_counts_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts) {
    return at::_ops::batch_norm_gather_stats_with_counts_out::call(input, mean, invstd, running_mean, running_var, momentum, eps, counts, out0, out1);
}
// aten::batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_gather_stats_with_counts_outf(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, const at::Tensor & counts, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::batch_norm_gather_stats_with_counts_out::call(input, mean, invstd, running_mean, running_var, momentum, eps, counts, out0, out1);
}

// aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask) {
    return at::_ops::native_batch_norm_backward_out::call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask, out0, out1, out2);
}
// aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> native_batch_norm_backward_outf(const at::Tensor & grad_out, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, const ::std::optional<at::Tensor> & save_mean, const ::std::optional<at::Tensor> & save_invstd, bool train, double eps, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::native_batch_norm_backward_out::call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask, out0, out1, out2);
}

// aten::batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> batch_norm_backward_reduce_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g) {
    return at::_ops::batch_norm_backward_reduce_out::call(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g, out0, out1, out2, out3);
}
// aten::batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> batch_norm_backward_reduce_outf(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, bool input_g, bool weight_g, bool bias_g, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) {
    return at::_ops::batch_norm_backward_reduce_out::call(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g, out0, out1, out2, out3);
}

// aten::batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & batch_norm_backward_elemt_out(at::Tensor & out, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count) {
    return at::_ops::batch_norm_backward_elemt_out::call(grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count, out);
}
// aten::batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & batch_norm_backward_elemt_outf(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const ::std::optional<at::Tensor> & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count, at::Tensor & out) {
    return at::_ops::batch_norm_backward_elemt_out::call(grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count, out);
}

// aten::batch_norm_update_stats.out(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_update_stats_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum) {
    return at::_ops::batch_norm_update_stats_out::call(input, running_mean, running_var, momentum, out0, out1);
}
// aten::batch_norm_update_stats.out(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> batch_norm_update_stats_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::batch_norm_update_stats_out::call(input, running_mean, running_var, momentum, out0, out1);
}

// aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nnpack_spatial_convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride=1) {
    return at::_ops::_nnpack_spatial_convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _nnpack_spatial_convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride=1) {
    return at::_ops::_nnpack_spatial_convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nnpack_spatial_convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::_nnpack_spatial_convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _nnpack_spatial_convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::_nnpack_spatial_convolution_out::call(input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nnpack_spatial_convolution_symint_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride=c10::SymInt(1)) {
    return at::_ops::_nnpack_spatial_convolution_out::call(input, weight, bias, padding, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _nnpack_spatial_convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride=c10::SymInt(1)) {
    return at::_ops::_nnpack_spatial_convolution_out::call(input, weight, bias, padding, stride, out);
  }
}

// aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nnpack_spatial_convolution_symint_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::_nnpack_spatial_convolution_out::call(input, weight, bias, padding, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _nnpack_spatial_convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::_nnpack_spatial_convolution_out::call(input, weight, bias, padding, stride, out);
  }
}

// aten::ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ones_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::ones_names_out::call(size, names, out);
}
// aten::ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ones_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::ones_names_out::call(size, names, out);
}

// aten::ones_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ones_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::ones_like_out::call(self, memory_format, out);
}
// aten::ones_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ones_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::ones_like_out::call(self, memory_format, out);
}

// aten::_euclidean_dist.out(Tensor x1, Tensor x2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _euclidean_dist_out(at::Tensor & out, const at::Tensor & x1, const at::Tensor & x2) {
    return at::_ops::_euclidean_dist_out::call(x1, x2, out);
}
// aten::_euclidean_dist.out(Tensor x1, Tensor x2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _euclidean_dist_outf(const at::Tensor & x1, const at::Tensor & x2, at::Tensor & out) {
    return at::_ops::_euclidean_dist_out::call(x1, x2, out);
}

// aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cdist_forward_out(at::Tensor & out, const at::Tensor & x1, const at::Tensor & x2, double p, ::std::optional<int64_t> compute_mode) {
    return at::_ops::_cdist_forward_out::call(x1, x2, p, compute_mode, out);
}
// aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cdist_forward_outf(const at::Tensor & x1, const at::Tensor & x2, double p, ::std::optional<int64_t> compute_mode, at::Tensor & out) {
    return at::_ops::_cdist_forward_out::call(x1, x2, p, compute_mode, out);
}

// aten::_cdist_backward.out(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cdist_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist) {
    return at::_ops::_cdist_backward_out::call(grad, x1, x2, p, cdist, out);
}
// aten::_cdist_backward.out(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cdist_backward_outf(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist, at::Tensor & out) {
    return at::_ops::_cdist_backward_out::call(grad, x1, x2, p, cdist, out);
}

// aten::_pdist_forward.out(Tensor self, float p=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _pdist_forward_out(at::Tensor & out, const at::Tensor & self, double p=2) {
    return at::_ops::_pdist_forward_out::call(self, p, out);
}
// aten::_pdist_forward.out(Tensor self, float p=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _pdist_forward_outf(const at::Tensor & self, double p, at::Tensor & out) {
    return at::_ops::_pdist_forward_out::call(self, p, out);
}

// aten::_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _pdist_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) {
    return at::_ops::_pdist_backward_out::call(grad, self, p, pdist, out);
}
// aten::_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _pdist_backward_outf(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist, at::Tensor & out) {
    return at::_ops::_pdist_backward_out::call(grad, self, p, pdist, out);
}

// aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pixel_shuffle_out(at::Tensor & out, const at::Tensor & self, int64_t upscale_factor) {
    return at::_ops::pixel_shuffle_out::call(self, upscale_factor, out);
}
// aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pixel_shuffle_outf(const at::Tensor & self, int64_t upscale_factor, at::Tensor & out) {
    return at::_ops::pixel_shuffle_out::call(self, upscale_factor, out);
}

// aten::pixel_unshuffle.out(Tensor self, int downscale_factor, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pixel_unshuffle_out(at::Tensor & out, const at::Tensor & self, int64_t downscale_factor) {
    return at::_ops::pixel_unshuffle_out::call(self, downscale_factor, out);
}
// aten::pixel_unshuffle.out(Tensor self, int downscale_factor, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & pixel_unshuffle_outf(const at::Tensor & self, int64_t downscale_factor, at::Tensor & out) {
    return at::_ops::pixel_unshuffle_out::call(self, downscale_factor, out);
}

// aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & channel_shuffle_out(at::Tensor & out, const at::Tensor & self, int64_t groups) {
    return at::_ops::channel_shuffle_out::call(self, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & channel_shuffle_out(at::Tensor & out, const at::Tensor & self, int64_t groups) {
    return at::_ops::channel_shuffle_out::call(self, groups, out);
  }
}

// aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & channel_shuffle_outf(const at::Tensor & self, int64_t groups, at::Tensor & out) {
    return at::_ops::channel_shuffle_out::call(self, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & channel_shuffle_outf(const at::Tensor & self, int64_t groups, at::Tensor & out) {
    return at::_ops::channel_shuffle_out::call(self, groups, out);
  }
}

// aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & channel_shuffle_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymInt groups) {
    return at::_ops::channel_shuffle_out::call(self, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & channel_shuffle_out(at::Tensor & out, const at::Tensor & self, c10::SymInt groups) {
    return at::_ops::channel_shuffle_out::call(self, groups, out);
  }
}

// aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & channel_shuffle_symint_outf(const at::Tensor & self, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::channel_shuffle_out::call(self, groups, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & channel_shuffle_outf(const at::Tensor & self, c10::SymInt groups, at::Tensor & out) {
    return at::_ops::channel_shuffle_out::call(self, groups, out);
  }
}

// aten::_pin_memory.out(Tensor self, Device? device=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _pin_memory_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Device> device=::std::nullopt) {
    return at::_ops::_pin_memory_out::call(self, device, out);
}
// aten::_pin_memory.out(Tensor self, Device? device=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _pin_memory_outf(const at::Tensor & self, ::std::optional<at::Device> device, at::Tensor & out) {
    return at::_ops::_pin_memory_out::call(self, device, out);
}

// aten::scalar_tensor.out(Scalar s, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scalar_tensor_out(at::Tensor & out, const at::Scalar & s) {
    return at::_ops::scalar_tensor_out::call(s, out);
}
// aten::scalar_tensor.out(Scalar s, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & scalar_tensor_outf(const at::Scalar & s, at::Tensor & out) {
    return at::_ops::scalar_tensor_out::call(s, out);
}

// aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::rand_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::rand_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
  }
}

// aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::rand_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::rand_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
  }
}

// aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::rand_names_out::call(size, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & rand_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::rand_names_out::call(size, names, out);
  }
}

// aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::rand_names_out::call(size, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & rand_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::rand_names_out::call(size, names, out);
  }
}

// aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
    return at::_ops::rand_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
    return at::_ops::rand_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
  }
}

// aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::rand_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::rand_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
  }
}

// aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
    return at::_ops::rand_generator_with_names_out::call(size, generator, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & rand_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
    return at::_ops::rand_generator_with_names_out::call(size, generator, names, out);
  }
}

// aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::rand_generator_with_names_out::call(size, generator, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & rand_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::rand_generator_with_names_out::call(size, generator, names, out);
  }
}

// aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::rand_like_out::call(self, memory_format, out);
}
// aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rand_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::rand_like_out::call(self, memory_format, out);
}

// aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_out::call(self, high, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_out::call(self, high, memory_format, out);
  }
}

// aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_like_outf(const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randint_like_out::call(self, high, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_like_outf(const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randint_like_out::call(self, high, memory_format, out);
  }
}

// aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_like_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_out::call(self, high, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_out::call(self, high, memory_format, out);
  }
}

// aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_like_symint_outf(const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randint_like_out::call(self, high, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_like_outf(const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randint_like_out::call(self, high, memory_format, out);
  }
}

// aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
  }
}

// aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_like_outf(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randint_like_outf(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
  }
}

// aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_like_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
  }
}

// aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randint_like_symint_outf(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randint_like_outf(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
  }
}

// aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::randn_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::randn_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
  }
}

// aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::randn_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::randn_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
  }
}

// aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::randn_names_out::call(size, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randn_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::randn_names_out::call(size, names, out);
  }
}

// aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::randn_names_out::call(size, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randn_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::randn_names_out::call(size, names, out);
  }
}

// aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
    return at::_ops::randn_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
    return at::_ops::randn_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
  }
}

// aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::randn_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::randn_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
  }
}

// aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
    return at::_ops::randn_generator_with_names_out::call(size, generator, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randn_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
    return at::_ops::randn_generator_with_names_out::call(size, generator, names, out);
  }
}

// aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::randn_generator_with_names_out::call(size, generator, names, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & randn_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::randn_generator_with_names_out::call(size, generator, names, out);
  }
}

// aten::randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::randn_like_out::call(self, memory_format, out);
}
// aten::randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & randn_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::randn_like_out::call(self, memory_format, out);
}

// aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & repeat_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef repeats) {
    return at::_ops::repeat_out::call(self, c10::fromIntArrayRefSlow(repeats), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & repeat_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef repeats) {
    return at::_ops::repeat_out::call(self, c10::fromIntArrayRefSlow(repeats), out);
  }
}

// aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & repeat_outf(const at::Tensor & self, at::IntArrayRef repeats, at::Tensor & out) {
    return at::_ops::repeat_out::call(self, c10::fromIntArrayRefSlow(repeats), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & repeat_outf(const at::Tensor & self, at::IntArrayRef repeats, at::Tensor & out) {
    return at::_ops::repeat_out::call(self, c10::fromIntArrayRefSlow(repeats), out);
  }
}

// aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & repeat_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef repeats) {
    return at::_ops::repeat_out::call(self, repeats, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & repeat_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef repeats) {
    return at::_ops::repeat_out::call(self, repeats, out);
  }
}

// aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & repeat_symint_outf(const at::Tensor & self, c10::SymIntArrayRef repeats, at::Tensor & out) {
    return at::_ops::repeat_out::call(self, repeats, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & repeat_outf(const at::Tensor & self, c10::SymIntArrayRef repeats, at::Tensor & out) {
    return at::_ops::repeat_out::call(self, repeats, out);
  }
}

// aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & repeat_interleave_out(at::Tensor & out, const at::Tensor & repeats, ::std::optional<int64_t> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_Tensor_out::call(repeats, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & repeat_interleave_out(at::Tensor & out, const at::Tensor & repeats, ::std::optional<int64_t> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_Tensor_out::call(repeats, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt, out);
  }
}

// aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & repeat_interleave_outf(const at::Tensor & repeats, ::std::optional<int64_t> output_size, at::Tensor & out) {
    return at::_ops::repeat_interleave_Tensor_out::call(repeats, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & repeat_interleave_outf(const at::Tensor & repeats, ::std::optional<int64_t> output_size, at::Tensor & out) {
    return at::_ops::repeat_interleave_Tensor_out::call(repeats, output_size.has_value() ? ::std::make_optional(c10::SymInt(*output_size)) : ::std::nullopt, out);
  }
}

// aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & repeat_interleave_symint_out(at::Tensor & out, const at::Tensor & repeats, ::std::optional<c10::SymInt> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_Tensor_out::call(repeats, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & repeat_interleave_out(at::Tensor & out, const at::Tensor & repeats, ::std::optional<c10::SymInt> output_size=::std::nullopt) {
    return at::_ops::repeat_interleave_Tensor_out::call(repeats, output_size, out);
  }
}

// aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & repeat_interleave_symint_outf(const at::Tensor & repeats, ::std::optional<c10::SymInt> output_size, at::Tensor & out) {
    return at::_ops::repeat_interleave_Tensor_out::call(repeats, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & repeat_interleave_outf(const at::Tensor & repeats, ::std::optional<c10::SymInt> output_size, at::Tensor & out) {
    return at::_ops::repeat_interleave_Tensor_out::call(repeats, output_size, out);
  }
}

// aten::_mkldnn_reshape.out(Tensor self, int[] shape, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mkldnn_reshape_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef shape) {
    return at::_ops::_mkldnn_reshape_out::call(self, shape, out);
}
// aten::_mkldnn_reshape.out(Tensor self, int[] shape, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mkldnn_reshape_outf(const at::Tensor & self, at::IntArrayRef shape, at::Tensor & out) {
    return at::_ops::_mkldnn_reshape_out::call(self, shape, out);
}

// aten::relu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & relu_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::relu_out::call(self, out);
}
// aten::relu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & relu_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::relu_out::call(self, out);
}

// aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index) {
    return at::_ops::select_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & select_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index) {
    return at::_ops::select_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index, out);
  }
}

// aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index, at::Tensor & out) {
    return at::_ops::select_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & select_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index, at::Tensor & out) {
    return at::_ops::select_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index, out);
  }
}

// aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_backward_symint_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) {
    return at::_ops::select_backward_out::call(grad_output, input_sizes, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & select_backward_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) {
    return at::_ops::select_backward_out::call(grad_output, input_sizes, dim, index, out);
  }
}

// aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out) {
    return at::_ops::select_backward_out::call(grad_output, input_sizes, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & select_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out) {
    return at::_ops::select_backward_out::call(grad_output, input_sizes, dim, index, out);
  }
}

// aten::celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & celu_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & alpha=1.0) {
    return at::_ops::celu_out::call(self, alpha, out);
}
// aten::celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & celu_outf(const at::Tensor & self, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::celu_out::call(self, alpha, out);
}

// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) {
    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
  }
}

// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step, at::Tensor & out) {
    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slice_backward_outf(const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step, at::Tensor & out) {
    return at::_ops::slice_backward_out::call(grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out);
  }
}

// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_backward_symint_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slice_backward_out(at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) {
    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
  }
}

// aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) {
    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slice_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) {
    return at::_ops::slice_backward_out::call(grad_output, input_sizes, dim, start, end, step, out);
  }
}

// aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_scatter_out::call(self, src, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slice_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_scatter_out::call(self, src, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step, out);
  }
}

// aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional<int64_t> start, ::std::optional<int64_t> end, int64_t step, at::Tensor & out) {
    return at::_ops::slice_scatter_out::call(self, src, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slice_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional<int64_t> start, ::std::optional<int64_t> end, int64_t step, at::Tensor & out) {
    return at::_ops::slice_scatter_out::call(self, src, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step, out);
  }
}

// aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_scatter_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_scatter_out::call(self, src, dim, start, end, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slice_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_scatter_out::call(self, src, dim, start, end, step, out);
  }
}

// aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_scatter_symint_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) {
    return at::_ops::slice_scatter_out::call(self, src, dim, start, end, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slice_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) {
    return at::_ops::slice_scatter_out::call(self, src, dim, start, end, step, out);
  }
}

// aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index) {
    return at::_ops::select_scatter_out::call(self, src, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & select_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index) {
    return at::_ops::select_scatter_out::call(self, src, dim, index, out);
  }
}

// aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index, at::Tensor & out) {
    return at::_ops::select_scatter_out::call(self, src, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & select_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index, at::Tensor & out) {
    return at::_ops::select_scatter_out::call(self, src, dim, index, out);
  }
}

// aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_scatter_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index) {
    return at::_ops::select_scatter_out::call(self, src, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & select_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index) {
    return at::_ops::select_scatter_out::call(self, src, dim, index, out);
  }
}

// aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_scatter_symint_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index, at::Tensor & out) {
    return at::_ops::select_scatter_out::call(self, src, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & select_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index, at::Tensor & out) {
    return at::_ops::select_scatter_out::call(self, src, dim, index, out);
  }
}

// aten::diagonal_scatter.out(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diagonal_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) {
    return at::_ops::diagonal_scatter_out::call(self, src, offset, dim1, dim2, out);
}
// aten::diagonal_scatter.out(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diagonal_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) {
    return at::_ops::diagonal_scatter_out::call(self, src, offset, dim1, dim2, out);
}

// aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & as_strided_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_scatter_out::call(self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & as_strided_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_scatter_out::call(self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt, out);
  }
}

// aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & as_strided_scatter_outf(const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset, at::Tensor & out) {
    return at::_ops::as_strided_scatter_out::call(self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & as_strided_scatter_outf(const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset, at::Tensor & out) {
    return at::_ops::as_strided_scatter_out::call(self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt, out);
  }
}

// aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & as_strided_scatter_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_scatter_out::call(self, src, size, stride, storage_offset, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & as_strided_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_scatter_out::call(self, src, size, stride, storage_offset, out);
  }
}

// aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & as_strided_scatter_symint_outf(const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset, at::Tensor & out) {
    return at::_ops::as_strided_scatter_out::call(self, src, size, stride, storage_offset, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & as_strided_scatter_outf(const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset, at::Tensor & out) {
    return at::_ops::as_strided_scatter_out::call(self, src, size, stride, storage_offset, out);
  }
}

// aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unsafe_split_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void unsafe_split_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) {
    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
  }
}

// aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unsafe_split_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) {
    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void unsafe_split_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) {
    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
  }
}

// aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unsafe_split_symint_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void unsafe_split_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
  }
}

// aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unsafe_split_symint_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) {
    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void unsafe_split_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) {
    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
  }
}

// aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unsafe_split_with_sizes_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::unsafe_split_with_sizes_out::call(self, c10::fromIntArrayRefSlow(split_sizes), dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void unsafe_split_with_sizes_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::unsafe_split_with_sizes_out::call(self, c10::fromIntArrayRefSlow(split_sizes), dim, out);
  }
}

// aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unsafe_split_with_sizes_outf(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out) {
    return at::_ops::unsafe_split_with_sizes_out::call(self, c10::fromIntArrayRefSlow(split_sizes), dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  void unsafe_split_with_sizes_outf(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out) {
    return at::_ops::unsafe_split_with_sizes_out::call(self, c10::fromIntArrayRefSlow(split_sizes), dim, out);
  }
}

// aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unsafe_split_with_sizes_symint_out(at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::unsafe_split_with_sizes_out::call(self, split_sizes, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void unsafe_split_with_sizes_out(at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
    return at::_ops::unsafe_split_with_sizes_out::call(self, split_sizes, dim, out);
  }
}

// aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()
inline void unsafe_split_with_sizes_symint_outf(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) {
    return at::_ops::unsafe_split_with_sizes_out::call(self, split_sizes, dim, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  void unsafe_split_with_sizes_outf(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) {
    return at::_ops::unsafe_split_with_sizes_out::call(self, split_sizes, dim, out);
  }
}

// aten::sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::sum_out::call(self, dtype, out);
}
// aten::sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sum_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::sum_out::call(self, dtype, out);
}

// aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> std_mean_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::std_mean_correction_out::call(self, dim, correction, keepdim, out0, out1);
}
// aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> std_mean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::std_mean_correction_out::call(self, dim, correction, keepdim, out0, out1);
}

// aten::prod.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & prod_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::prod_out::call(self, dtype, out);
}
// aten::prod.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & prod_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::prod_out::call(self, dtype, out);
}

// aten::_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mkldnn_transpose_out(at::Tensor & out, const at::Tensor & self, int64_t dim0, int64_t dim1) {
    return at::_ops::_mkldnn_transpose_out::call(self, dim0, dim1, out);
}
// aten::_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _mkldnn_transpose_outf(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) {
    return at::_ops::_mkldnn_transpose_out::call(self, dim0, dim1, out);
}

// aten::flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & flip_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dims) {
    return at::_ops::flip_out::call(self, dims, out);
}
// aten::flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & flip_outf(const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out) {
    return at::_ops::flip_out::call(self, dims, out);
}

// aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & roll_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims={}) {
    return at::_ops::roll_out::call(self, c10::fromIntArrayRefSlow(shifts), dims, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & roll_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims={}) {
    return at::_ops::roll_out::call(self, c10::fromIntArrayRefSlow(shifts), dims, out);
  }
}

// aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & roll_outf(const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) {
    return at::_ops::roll_out::call(self, c10::fromIntArrayRefSlow(shifts), dims, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & roll_outf(const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) {
    return at::_ops::roll_out::call(self, c10::fromIntArrayRefSlow(shifts), dims, out);
  }
}

// aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & roll_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims={}) {
    return at::_ops::roll_out::call(self, shifts, dims, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & roll_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims={}) {
    return at::_ops::roll_out::call(self, shifts, dims, out);
  }
}

// aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & roll_symint_outf(const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) {
    return at::_ops::roll_out::call(self, shifts, dims, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & roll_outf(const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) {
    return at::_ops::roll_out::call(self, shifts, dims, out);
  }
}

// aten::rot90.out(Tensor self, int k=1, int[] dims=[0,1], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rot90_out(at::Tensor & out, const at::Tensor & self, int64_t k=1, at::IntArrayRef dims={0,1}) {
    return at::_ops::rot90_out::call(self, k, dims, out);
}
// aten::rot90.out(Tensor self, int k=1, int[] dims=[0,1], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rot90_outf(const at::Tensor & self, int64_t k, at::IntArrayRef dims, at::Tensor & out) {
    return at::_ops::rot90_out::call(self, k, dims, out);
}

// aten::_transform_bias_rescale_qkv.out(Tensor qkv, Tensor qkv_bias, int num_heads, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _transform_bias_rescale_qkv_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads) {
    return at::_ops::_transform_bias_rescale_qkv_out::call(qkv, qkv_bias, num_heads, out0, out1, out2);
}
// aten::_transform_bias_rescale_qkv.out(Tensor qkv, Tensor qkv_bias, int num_heads, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _transform_bias_rescale_qkv_outf(const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_transform_bias_rescale_qkv_out::call(qkv, qkv_bias, num_heads, out0, out1, out2);
}

// aten::_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_from_mask_out(at::Tensor & out, const at::Tensor & t, const at::Tensor & mask, bool mask_check=true) {
    return at::_ops::_nested_tensor_from_mask_out::call(t, mask, mask_check, out);
}
// aten::_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_from_mask_outf(const at::Tensor & t, const at::Tensor & mask, bool mask_check, at::Tensor & out) {
    return at::_ops::_nested_tensor_from_mask_out::call(t, mask, mask_check, out);
}

// aten::_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_from_padded_out(at::Tensor & out, const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213=false) {
    return at::_ops::_nested_from_padded_out::call(padded, cpu_nested_shape_example, fuse_transform_0213, out);
}
// aten::_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_from_padded_outf(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213, at::Tensor & out) {
    return at::_ops::_nested_from_padded_out::call(padded, cpu_nested_shape_example, fuse_transform_0213, out);
}

// aten::_nested_tensor_size.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_size_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_nested_tensor_size_out::call(self, out);
}
// aten::_nested_tensor_size.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_size_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_nested_tensor_size_out::call(self, out);
}

// aten::_nested_tensor_strides.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_strides_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_nested_tensor_strides_out::call(self, out);
}
// aten::_nested_tensor_strides.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_strides_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_nested_tensor_strides_out::call(self, out);
}

// aten::_nested_tensor_storage_offsets.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_storage_offsets_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_nested_tensor_storage_offsets_out::call(self, out);
}
// aten::_nested_tensor_storage_offsets.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_storage_offsets_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_nested_tensor_storage_offsets_out::call(self, out);
}

// aten::_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_from_padded_and_nested_example_out(at::Tensor & out, const at::Tensor & padded, const at::Tensor & nt_example) {
    return at::_ops::_nested_from_padded_and_nested_example_out::call(padded, nt_example, out);
}
// aten::_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_from_padded_and_nested_example_outf(const at::Tensor & padded, const at::Tensor & nt_example, at::Tensor & out) {
    return at::_ops::_nested_from_padded_and_nested_example_out::call(padded, nt_example, out);
}

// aten::_nested_view_from_buffer_copy.out(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_view_from_buffer_copy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) {
    return at::_ops::_nested_view_from_buffer_copy_out::call(self, nested_size, nested_strides, offsets, out);
}
// aten::_nested_view_from_buffer_copy.out(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_view_from_buffer_copy_outf(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets, at::Tensor & out) {
    return at::_ops::_nested_view_from_buffer_copy_out::call(self, nested_size, nested_strides, offsets, out);
}

// aten::_nested_view_from_jagged_copy.out(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_view_from_jagged_copy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional<at::Tensor> & lengths={}, int64_t ragged_idx=1, const ::std::optional<at::Tensor> & min_seqlen={}, const ::std::optional<at::Tensor> & max_seqlen={}) {
    return at::_ops::_nested_view_from_jagged_copy_out::call(self, offsets, dummy, lengths, ragged_idx, min_seqlen, max_seqlen, out);
}
// aten::_nested_view_from_jagged_copy.out(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, Tensor? min_seqlen=None, Tensor? max_seqlen=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_view_from_jagged_copy_outf(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const ::std::optional<at::Tensor> & lengths, int64_t ragged_idx, const ::std::optional<at::Tensor> & min_seqlen, const ::std::optional<at::Tensor> & max_seqlen, at::Tensor & out) {
    return at::_ops::_nested_view_from_jagged_copy_out::call(self, offsets, dummy, lengths, ragged_idx, min_seqlen, max_seqlen, out);
}

// aten::_nested_get_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_get_values_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_nested_get_values_copy_out::call(self, out);
}
// aten::_nested_get_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_get_values_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_nested_get_values_copy_out::call(self, out);
}

// aten::_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _trilinear_out(at::Tensor & out, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim=1) {
    return at::_ops::_trilinear_out::call(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim, out);
}
// aten::_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _trilinear_outf(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim, at::Tensor & out) {
    return at::_ops::_trilinear_out::call(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim, out);
}

// aten::_unique.out(Tensor self, bool sorted=True, bool return_inverse=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _unique_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, bool sorted=true, bool return_inverse=false) {
    return at::_ops::_unique_out::call(self, sorted, return_inverse, out0, out1);
}
// aten::_unique.out(Tensor self, bool sorted=True, bool return_inverse=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _unique_outf(const at::Tensor & self, bool sorted, bool return_inverse, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_unique_out::call(self, sorted, return_inverse, out0, out1);
}

// aten::unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false) {
    return at::_ops::unique_dim_out::call(self, dim, sorted, return_inverse, return_counts, out0, out1, out2);
}
// aten::unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_outf(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::unique_dim_out::call(self, dim, sorted, return_inverse, return_counts, out0, out1, out2);
}

// aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::unique_consecutive_out::call(self, return_inverse, return_counts, dim, out0, out1, out2);
}
// aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_outf(const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::unique_consecutive_out::call(self, return_inverse, return_counts, dim, out0, out1, out2);
}

// aten::unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_consecutive_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false) {
    return at::_ops::unique_dim_consecutive_out::call(self, dim, return_inverse, return_counts, out0, out1, out2);
}
// aten::unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_consecutive_outf(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::unique_dim_consecutive_out::call(self, dim, return_inverse, return_counts, out0, out1, out2);
}

// aten::_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _unique2_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, bool sorted=true, bool return_inverse=false, bool return_counts=false) {
    return at::_ops::_unique2_out::call(self, sorted, return_inverse, return_counts, out0, out1, out2);
}
// aten::_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _unique2_outf(const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_unique2_out::call(self, sorted, return_inverse, return_counts, out0, out1, out2);
}

// aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _unsafe_view_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_unsafe_view_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _unsafe_view_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_unsafe_view_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _unsafe_view_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::_unsafe_view_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _unsafe_view_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::_unsafe_view_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _unsafe_view_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::_unsafe_view_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _unsafe_view_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::_unsafe_view_out::call(self, size, out);
  }
}

// aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _unsafe_view_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::_unsafe_view_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _unsafe_view_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::_unsafe_view_out::call(self, size, out);
  }
}

// aten::var_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> var_mean_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
    return at::_ops::var_mean_correction_out::call(self, dim, correction, keepdim, out0, out1);
}
// aten::var_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> var_mean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::var_mean_correction_out::call(self, dim, correction, keepdim, out0, out1);
}

// aten::_weight_norm_interface.out(Tensor v, Tensor g, int dim=0, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & v, const at::Tensor & g, int64_t dim=0) {
    return at::_ops::_weight_norm_interface_out::call(v, g, dim, out0, out1);
}
// aten::_weight_norm_interface.out(Tensor v, Tensor g, int dim=0, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_outf(const at::Tensor & v, const at::Tensor & g, int64_t dim, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_weight_norm_interface_out::call(v, g, dim, out0, out1);
}

// aten::_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_backward_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) {
    return at::_ops::_weight_norm_interface_backward_out::call(grad_w, saved_v, saved_g, saved_norms, dim, out0, out1);
}
// aten::_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _weight_norm_interface_backward_outf(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_weight_norm_interface_backward_out::call(grad_w, saved_v, saved_g, saved_norms, dim, out0, out1);
}

// aten::zeros.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
    return at::_ops::zeros_names_out::call(size, names, out);
}
// aten::zeros.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zeros_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
    return at::_ops::zeros_names_out::call(size, names, out);
}

// aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _efficientzerotensor_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::_efficientzerotensor_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _efficientzerotensor_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::_efficientzerotensor_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _efficientzerotensor_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::_efficientzerotensor_out::call(c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _efficientzerotensor_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::_efficientzerotensor_out::call(c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _efficientzerotensor_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::_efficientzerotensor_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _efficientzerotensor_out(at::Tensor & out, c10::SymIntArrayRef size) {
    return at::_ops::_efficientzerotensor_out::call(size, out);
  }
}

// aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _efficientzerotensor_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::_efficientzerotensor_out::call(size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _efficientzerotensor_outf(c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::_efficientzerotensor_out::call(size, out);
  }
}

// aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zeros_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::zeros_like_out::call(self, memory_format, out);
}
// aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zeros_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::zeros_like_out::call(self, memory_format, out);
}

// aten::_standard_gamma_grad.out(Tensor self, Tensor output, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _standard_gamma_grad_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & output) {
    return at::_ops::_standard_gamma_grad_out::call(self, output, out);
}
// aten::_standard_gamma_grad.out(Tensor self, Tensor output, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _standard_gamma_grad_outf(const at::Tensor & self, const at::Tensor & output, at::Tensor & out) {
    return at::_ops::_standard_gamma_grad_out::call(self, output, out);
}

// aten::_standard_gamma.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _standard_gamma_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::_standard_gamma_out::call(self, generator, out);
}
// aten::_standard_gamma.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _standard_gamma_outf(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::_standard_gamma_out::call(self, generator, out);
}

// aten::_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _dirichlet_grad_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) {
    return at::_ops::_dirichlet_grad_out::call(x, alpha, total, out);
}
// aten::_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _dirichlet_grad_outf(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total, at::Tensor & out) {
    return at::_ops::_dirichlet_grad_out::call(x, alpha, total, out);
}

// aten::_sample_dirichlet.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sample_dirichlet_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::_sample_dirichlet_out::call(self, generator, out);
}
// aten::_sample_dirichlet.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sample_dirichlet_outf(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::_sample_dirichlet_out::call(self, generator, out);
}

// aten::poisson.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & poisson_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::poisson_out::call(self, generator, out);
}
// aten::poisson.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & poisson_outf(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::poisson_out::call(self, generator, out);
}

// aten::binomial.out(Tensor count, Tensor prob, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & binomial_out(at::Tensor & out, const at::Tensor & count, const at::Tensor & prob, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::binomial_out::call(count, prob, generator, out);
}
// aten::binomial.out(Tensor count, Tensor prob, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & binomial_outf(const at::Tensor & count, const at::Tensor & prob, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::binomial_out::call(count, prob, generator, out);
}

// aten::native_norm.out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & native_norm_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & p=2) {
    return at::_ops::native_norm_out::call(self, p, out);
}
// aten::native_norm.out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & native_norm_outf(const at::Tensor & self, const at::Scalar & p, at::Tensor & out) {
    return at::_ops::native_norm_out::call(self, p, out);
}

// aten::native_norm.ScalarOpt_dim_dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & native_norm_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype) {
    return at::_ops::native_norm_ScalarOpt_dim_dtype_out::call(self, p, dim, keepdim, dtype, out);
}
// aten::native_norm.ScalarOpt_dim_dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & native_norm_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::native_norm_ScalarOpt_dim_dtype_out::call(self, p, dim, keepdim, dtype, out);
}

// aten::_batch_norm_with_update_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _batch_norm_with_update_functional(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) {
    return at::_ops::_batch_norm_with_update_functional::call(input, weight, bias, running_mean, running_var, momentum, eps);
}

// aten::_batch_norm_no_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _batch_norm_no_update_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps) {
    return at::_ops::_batch_norm_no_update_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out0, out1, out2, out3);
}
// aten::_batch_norm_no_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _batch_norm_no_update_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & running_mean, const ::std::optional<at::Tensor> & running_var, double momentum, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) {
    return at::_ops::_batch_norm_no_update_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out0, out1, out2, out3);
}

// aten::_sparse_sum.dim_out(Tensor self, int[1] dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_sum_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::_sparse_sum_dim_out::call(self, dim, out);
}
// aten::_sparse_sum.dim_out(Tensor self, int[1] dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_sum_outf(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) {
    return at::_ops::_sparse_sum_dim_out::call(self, dim, out);
}

// aten::_sparse_sum_backward.out(Tensor grad, Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_sum_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::_sparse_sum_backward_out::call(grad, self, dim, out);
}
// aten::_sparse_sum_backward.out(Tensor grad, Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_sum_backward_outf(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) {
    return at::_ops::_sparse_sum_backward_out::call(grad, self, dim, out);
}

// aten::_sparse_csr_sum.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_csr_sum_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_sparse_csr_sum_dim_dtype_out::call(self, dim, keepdim, dtype, out);
}
// aten::_sparse_csr_sum.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_csr_sum_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::_sparse_csr_sum_dim_dtype_out::call(self, dim, keepdim, dtype, out);
}

// aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_csr_prod_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_sparse_csr_prod_dim_dtype_out::call(self, dim, keepdim, dtype, out);
}
// aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_csr_prod_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::_sparse_csr_prod_dim_dtype_out::call(self, dim, keepdim, dtype, out);
}

// aten::_sparse_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) {
    return at::_ops::_sparse_softmax_out::call(self, dim, half_to_float, out);
}
// aten::_sparse_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_softmax_outf(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) {
    return at::_ops::_sparse_softmax_out::call(self, dim, half_to_float, out);
}

// aten::_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_softmax_backward_data_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) {
    return at::_ops::_sparse_softmax_backward_data_out::call(grad_output, output, dim, self, out);
}
// aten::_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_softmax_backward_data_outf(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_sparse_softmax_backward_data_out::call(grad_output, output, dim, self, out);
}

// aten::_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_log_softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) {
    return at::_ops::_sparse_log_softmax_out::call(self, dim, half_to_float, out);
}
// aten::_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_log_softmax_outf(const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) {
    return at::_ops::_sparse_log_softmax_out::call(self, dim, half_to_float, out);
}

// aten::_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_log_softmax_backward_data_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) {
    return at::_ops::_sparse_log_softmax_backward_data_out::call(grad_output, output, dim, self, out);
}
// aten::_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_log_softmax_backward_data_outf(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_sparse_log_softmax_backward_data_out::call(grad_output, output, dim, self, out);
}

// aten::_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _spdiags_out(at::Tensor & out, const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, ::std::optional<at::Layout> layout=::std::nullopt) {
    return at::_ops::_spdiags_out::call(diagonals, offsets, shape, layout, out);
}
// aten::_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _spdiags_outf(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, ::std::optional<at::Layout> layout, at::Tensor & out) {
    return at::_ops::_spdiags_out::call(diagonals, offsets, shape, layout, out);
}

// aten::norm.ScalarOpt_dtype_out(Tensor self, Scalar? p, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_out(at::Tensor & out, const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::ScalarType dtype) {
    return at::_ops::norm_ScalarOpt_dtype_out::call(self, p, dtype, out);
}
// aten::norm.ScalarOpt_dtype_out(Tensor self, Scalar? p, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_outf(const at::Tensor & self, const ::std::optional<at::Scalar> & p, at::ScalarType dtype, at::Tensor & out) {
    return at::_ops::norm_ScalarOpt_dtype_out::call(self, p, dtype, out);
}

// aten::norm.Scalar_out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & p=2) {
    return at::_ops::norm_Scalar_out::call(self, p, out);
}
// aten::norm.Scalar_out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & norm_outf(const at::Tensor & self, const at::Scalar & p, at::Tensor & out) {
    return at::_ops::norm_Scalar_out::call(self, p, out);
}

// aten::clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clone_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::clone_out::call(self, memory_format, out);
}
// aten::clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & clone_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::clone_out::call(self, memory_format, out);
}

// aten::resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & resize_as_out(const at::Tensor & out, const at::Tensor & self, const at::Tensor & the_template, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_as_out::call(self, the_template, memory_format, out);
}
// aten::resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & resize_as_outf(const at::Tensor & self, const at::Tensor & the_template, ::std::optional<at::MemoryFormat> memory_format, const at::Tensor & out) {
    return at::_ops::resize_as_out::call(self, the_template, memory_format, out);
}

// aten::resize_as(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor
inline at::Tensor resize_as(const at::Tensor & self, const at::Tensor & the_template, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::resize_as::call(self, the_template, memory_format);
}

// aten::resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & resize_as_sparse_out(const at::Tensor & out, const at::Tensor & self, const at::Tensor & the_template) {
    return at::_ops::resize_as_sparse_out::call(self, the_template, out);
}
// aten::resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & resize_as_sparse_outf(const at::Tensor & self, const at::Tensor & the_template, const at::Tensor & out) {
    return at::_ops::resize_as_sparse_out::call(self, the_template, out);
}

// aten::resize_as_sparse(Tensor self, Tensor the_template) -> Tensor
inline at::Tensor resize_as_sparse(const at::Tensor & self, const at::Tensor & the_template) {
    return at::_ops::resize_as_sparse::call(self, the_template);
}

// aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zero_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::zero_out::call(self, out);
}
// aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & zero_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::zero_out::call(self, out);
}

// aten::zero(Tensor self) -> Tensor
inline at::Tensor zero(const at::Tensor & self) {
    return at::_ops::zero::call(self);
}

// aten::sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::sub_Scalar_out::call(self, other, alpha, out);
}
// aten::sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sub_outf(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::sub_Scalar_out::call(self, other, alpha, out);
}

// aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rsub_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::rsub_Tensor_out::call(self, other, alpha, out);
}
// aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rsub_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::rsub_Tensor_out::call(self, other, alpha, out);
}

// aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rsub_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
    return at::_ops::rsub_Scalar_out::call(self, other, alpha, out);
}
// aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rsub_outf(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::rsub_Scalar_out::call(self, other, alpha, out);
}

// aten::_sparse_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_addmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
    return at::_ops::_sparse_addmm_out::call(self, mat1, mat2, beta, alpha, out);
}
// aten::_sparse_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_addmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
    return at::_ops::_sparse_addmm_out::call(self, mat1, mat2, beta, alpha, out);
}

// aten::sparse_coo_tensor.size_out(int[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sparse_coo_tensor_out(at::Tensor & out, at::IntArrayRef size) {
    return at::_ops::sparse_coo_tensor_size_out::call(size, out);
}
// aten::sparse_coo_tensor.size_out(int[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sparse_coo_tensor_outf(at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::sparse_coo_tensor_size_out::call(size, out);
}

// aten::_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_coo_tensor_with_dims_out(at::Tensor & out, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size) {
    return at::_ops::_sparse_coo_tensor_with_dims_out::call(sparse_dim, dense_dim, size, out);
}
// aten::_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_coo_tensor_with_dims_outf(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::_sparse_coo_tensor_with_dims_out::call(sparse_dim, dense_dim, size, out);
}

// aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_out(at::Tensor & out, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::call(sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, is_coalesced, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_out(at::Tensor & out, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::call(sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, is_coalesced, out);
  }
}

// aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_outf(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced, at::Tensor & out) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::call(sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, is_coalesced, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_outf(int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced, at::Tensor & out) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::call(sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, is_coalesced, out);
  }
}

// aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_symint_out(at::Tensor & out, int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::call(sparse_dim, dense_dim, size, indices, values, is_coalesced, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_out(at::Tensor & out, int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced=::std::nullopt) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::call(sparse_dim, dense_dim, size, indices, values, is_coalesced, out);
  }
}

// aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_symint_outf(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced, at::Tensor & out) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::call(sparse_dim, dense_dim, size, indices, values, is_coalesced, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_outf(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, ::std::optional<bool> is_coalesced, at::Tensor & out) {
    return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::call(sparse_dim, dense_dim, size, indices, values, is_coalesced, out);
  }
}

// aten::sparse_resize.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & sparse_resize_out(const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) {
    return at::_ops::sparse_resize_out::call(self, size, sparse_dim, dense_dim, out);
}
// aten::sparse_resize.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & sparse_resize_outf(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim, const at::Tensor & out) {
    return at::_ops::sparse_resize_out::call(self, size, sparse_dim, dense_dim, out);
}

// aten::sparse_resize(Tensor self, int[] size, int sparse_dim, int dense_dim) -> Tensor
inline at::Tensor sparse_resize(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) {
    return at::_ops::sparse_resize::call(self, size, sparse_dim, dense_dim);
}

// aten::sparse_resize_and_clear.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & sparse_resize_and_clear_out(const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) {
    return at::_ops::sparse_resize_and_clear_out::call(self, size, sparse_dim, dense_dim, out);
}
// aten::sparse_resize_and_clear.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!)
inline const at::Tensor & sparse_resize_and_clear_outf(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim, const at::Tensor & out) {
    return at::_ops::sparse_resize_and_clear_out::call(self, size, sparse_dim, dense_dim, out);
}

// aten::sparse_resize_and_clear(Tensor self, int[] size, int sparse_dim, int dense_dim) -> Tensor
inline at::Tensor sparse_resize_and_clear(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) {
    return at::_ops::sparse_resize_and_clear::call(self, size, sparse_dim, dense_dim);
}

// aten::sparse_mask.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sparse_mask_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask) {
    return at::_ops::sparse_mask_out::call(self, mask, out);
}
// aten::sparse_mask.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & sparse_mask_outf(const at::Tensor & self, const at::Tensor & mask, at::Tensor & out) {
    return at::_ops::sparse_mask_out::call(self, mask, out);
}

// aten::_sparse_mask_projection.out(Tensor self, Tensor mask, bool accumulate_matches=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_mask_projection_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches=false) {
    return at::_ops::_sparse_mask_projection_out::call(self, mask, accumulate_matches, out);
}
// aten::_sparse_mask_projection.out(Tensor self, Tensor mask, bool accumulate_matches=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_mask_projection_outf(const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches, at::Tensor & out) {
    return at::_ops::_sparse_mask_projection_out::call(self, mask, accumulate_matches, out);
}

// aten::_to_dense.out(Tensor self, ScalarType? dtype=None, bool? masked_grad=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_dense_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt, ::std::optional<bool> masked_grad=::std::nullopt) {
    return at::_ops::_to_dense_out::call(self, dtype, masked_grad, out);
}
// aten::_to_dense.out(Tensor self, ScalarType? dtype=None, bool? masked_grad=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_dense_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<bool> masked_grad, at::Tensor & out) {
    return at::_ops::_to_dense_out::call(self, dtype, masked_grad, out);
}

// aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _coalesce_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_coalesce_out::call(self, out);
}
// aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _coalesce_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_coalesce_out::call(self, out);
}

// aten::_coalesced.out(Tensor self, bool coalesced, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _coalesced_out(at::Tensor & out, const at::Tensor & self, bool coalesced) {
    return at::_ops::_coalesced_out::call(self, coalesced, out);
}
// aten::_coalesced.out(Tensor self, bool coalesced, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _coalesced_outf(const at::Tensor & self, bool coalesced, at::Tensor & out) {
    return at::_ops::_coalesced_out::call(self, coalesced, out);
}

// aten::_coalesced(Tensor self, bool coalesced) -> Tensor
inline at::Tensor _coalesced(const at::Tensor & self, bool coalesced) {
    return at::_ops::_coalesced::call(self, coalesced);
}

// aten::copy_sparse_to_sparse.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & copy_sparse_to_sparse_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, bool non_blocking=false) {
    return at::_ops::copy_sparse_to_sparse_out::call(self, src, non_blocking, out);
}
// aten::copy_sparse_to_sparse.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & copy_sparse_to_sparse_outf(const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out) {
    return at::_ops::copy_sparse_to_sparse_out::call(self, src, non_blocking, out);
}

// aten::copy_sparse_to_sparse(Tensor self, Tensor src, bool non_blocking=False) -> Tensor
inline at::Tensor copy_sparse_to_sparse(const at::Tensor & self, const at::Tensor & src, bool non_blocking=false) {
    return at::_ops::copy_sparse_to_sparse::call(self, src, non_blocking);
}

// aten::_to_sparse.sparse_dim_out(Tensor self, int sparse_dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_out(at::Tensor & out, const at::Tensor & self, int64_t sparse_dim) {
    return at::_ops::_to_sparse_sparse_dim_out::call(self, sparse_dim, out);
}
// aten::_to_sparse.sparse_dim_out(Tensor self, int sparse_dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_outf(const at::Tensor & self, int64_t sparse_dim, at::Tensor & out) {
    return at::_ops::_to_sparse_sparse_dim_out::call(self, sparse_dim, out);
}

// aten::_to_sparse.out(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Layout> layout=::std::nullopt, at::OptionalIntArrayRef blocksize=::std::nullopt, ::std::optional<int64_t> dense_dim=::std::nullopt) {
    return at::_ops::_to_sparse_out::call(self, layout, blocksize, dense_dim, out);
}
// aten::_to_sparse.out(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_outf(const at::Tensor & self, ::std::optional<at::Layout> layout, at::OptionalIntArrayRef blocksize, ::std::optional<int64_t> dense_dim, at::Tensor & out) {
    return at::_ops::_to_sparse_out::call(self, layout, blocksize, dense_dim, out);
}

// aten::_to_sparse_csr.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_csr_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> dense_dim=::std::nullopt) {
    return at::_ops::_to_sparse_csr_out::call(self, dense_dim, out);
}
// aten::_to_sparse_csr.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_csr_outf(const at::Tensor & self, ::std::optional<int64_t> dense_dim, at::Tensor & out) {
    return at::_ops::_to_sparse_csr_out::call(self, dense_dim, out);
}

// aten::_to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_csc_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> dense_dim=::std::nullopt) {
    return at::_ops::_to_sparse_csc_out::call(self, dense_dim, out);
}
// aten::_to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_csc_outf(const at::Tensor & self, ::std::optional<int64_t> dense_dim, at::Tensor & out) {
    return at::_ops::_to_sparse_csc_out::call(self, dense_dim, out);
}

// aten::_to_sparse_bsr.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_bsr_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim=::std::nullopt) {
    return at::_ops::_to_sparse_bsr_out::call(self, blocksize, dense_dim, out);
}
// aten::_to_sparse_bsr.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_bsr_outf(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim, at::Tensor & out) {
    return at::_ops::_to_sparse_bsr_out::call(self, blocksize, dense_dim, out);
}

// aten::_to_sparse_bsc.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_bsc_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim=::std::nullopt) {
    return at::_ops::_to_sparse_bsc_out::call(self, blocksize, dense_dim, out);
}
// aten::_to_sparse_bsc.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_sparse_bsc_outf(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim, at::Tensor & out) {
    return at::_ops::_to_sparse_bsc_out::call(self, blocksize, dense_dim, out);
}

// aten::to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & to_mkldnn_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::to_mkldnn_out::call(self, dtype, out);
}
// aten::to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & to_mkldnn_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
    return at::_ops::to_mkldnn_out::call(self, dtype, out);
}

// aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_reorder_conv2d_weight_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv2d_weight_out::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & mkldnn_reorder_conv2d_weight_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv2d_weight_out::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt, out);
  }
}

// aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_reorder_conv2d_weight_outf(const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size, at::Tensor & out) {
    return at::_ops::mkldnn_reorder_conv2d_weight_out::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & mkldnn_reorder_conv2d_weight_outf(const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size, at::Tensor & out) {
    return at::_ops::mkldnn_reorder_conv2d_weight_out::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt, out);
  }
}

// aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_reorder_conv2d_weight_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv2d_weight_out::call(self, padding, stride, dilation, groups, input_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & mkldnn_reorder_conv2d_weight_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv2d_weight_out::call(self, padding, stride, dilation, groups, input_size, out);
  }
}

// aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_reorder_conv2d_weight_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size, at::Tensor & out) {
    return at::_ops::mkldnn_reorder_conv2d_weight_out::call(self, padding, stride, dilation, groups, input_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & mkldnn_reorder_conv2d_weight_outf(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size, at::Tensor & out) {
    return at::_ops::mkldnn_reorder_conv2d_weight_out::call(self, padding, stride, dilation, groups, input_size, out);
  }
}

// aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_reorder_conv3d_weight_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv3d_weight_out::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & mkldnn_reorder_conv3d_weight_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv3d_weight_out::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt, out);
  }
}

// aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_reorder_conv3d_weight_outf(const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size, at::Tensor & out) {
    return at::_ops::mkldnn_reorder_conv3d_weight_out::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & mkldnn_reorder_conv3d_weight_outf(const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size, at::Tensor & out) {
    return at::_ops::mkldnn_reorder_conv3d_weight_out::call(self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*input_size)) : ::std::nullopt, out);
  }
}

// aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_reorder_conv3d_weight_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv3d_weight_out::call(self, padding, stride, dilation, groups, input_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & mkldnn_reorder_conv3d_weight_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=::std::nullopt) {
    return at::_ops::mkldnn_reorder_conv3d_weight_out::call(self, padding, stride, dilation, groups, input_size, out);
  }
}

// aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_reorder_conv3d_weight_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size, at::Tensor & out) {
    return at::_ops::mkldnn_reorder_conv3d_weight_out::call(self, padding, stride, dilation, groups, input_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & mkldnn_reorder_conv3d_weight_outf(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size, at::Tensor & out) {
    return at::_ops::mkldnn_reorder_conv3d_weight_out::call(self, padding, stride, dilation, groups, input_size, out);
  }
}

// aten::quantize_per_tensor_dynamic.out(Tensor self, ScalarType dtype, bool reduce_range, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantize_per_tensor_dynamic_out(at::Tensor & out, const at::Tensor & self, at::ScalarType dtype, bool reduce_range) {
    return at::_ops::quantize_per_tensor_dynamic_out::call(self, dtype, reduce_range, out);
}
// aten::quantize_per_tensor_dynamic.out(Tensor self, ScalarType dtype, bool reduce_range, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantize_per_tensor_dynamic_outf(const at::Tensor & self, at::ScalarType dtype, bool reduce_range, at::Tensor & out) {
    return at::_ops::quantize_per_tensor_dynamic_out::call(self, dtype, reduce_range, out);
}

// aten::quantize_per_tensor.out(Tensor self, float scale, int zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantize_per_tensor_out(at::Tensor & out, const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype) {
    return at::_ops::quantize_per_tensor_out::call(self, scale, zero_point, dtype, out);
}
// aten::quantize_per_tensor.out(Tensor self, float scale, int zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantize_per_tensor_outf(const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype, at::Tensor & out) {
    return at::_ops::quantize_per_tensor_out::call(self, scale, zero_point, dtype, out);
}

// aten::quantize_per_tensor.tensor_qparams_out(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantize_per_tensor_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype) {
    return at::_ops::quantize_per_tensor_tensor_qparams_out::call(self, scale, zero_point, dtype, out);
}
// aten::quantize_per_tensor.tensor_qparams_out(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantize_per_tensor_outf(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype, at::Tensor & out) {
    return at::_ops::quantize_per_tensor_tensor_qparams_out::call(self, scale, zero_point, dtype, out);
}

// aten::quantize_per_tensor.tensors_out(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype, *, Tensor(a!)[] out) -> ()
inline void quantize_per_tensor_out(at::TensorList out, at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype) {
    return at::_ops::quantize_per_tensor_tensors_out::call(tensors, scales, zero_points, dtype, out);
}
// aten::quantize_per_tensor.tensors_out(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype, *, Tensor(a!)[] out) -> ()
inline void quantize_per_tensor_outf(at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype, at::TensorList out) {
    return at::_ops::quantize_per_tensor_tensors_out::call(tensors, scales, zero_points, dtype, out);
}

// aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantize_per_channel_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) {
    return at::_ops::quantize_per_channel_out::call(self, scales, zero_points, axis, dtype, out);
}
// aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & quantize_per_channel_outf(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype, at::Tensor & out) {
    return at::_ops::quantize_per_channel_out::call(self, scales, zero_points, axis, dtype, out);
}

// aten::dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & dequantize_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::dequantize_self_out::call(self, out);
}
// aten::dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & dequantize_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::dequantize_self_out::call(self, out);
}

// aten::dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> ()
inline void dequantize_out(at::TensorList out, at::TensorList tensors) {
    return at::_ops::dequantize_tensors_out::call(tensors, out);
}
// aten::dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> ()
inline void dequantize_outf(at::TensorList tensors, at::TensorList out) {
    return at::_ops::dequantize_tensors_out::call(tensors, out);
}

// aten::q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & q_per_channel_scales_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::q_per_channel_scales_out::call(self, out);
}
// aten::q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & q_per_channel_scales_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::q_per_channel_scales_out::call(self, out);
}

// aten::q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & q_per_channel_zero_points_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::q_per_channel_zero_points_out::call(self, out);
}
// aten::q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & q_per_channel_zero_points_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::q_per_channel_zero_points_out::call(self, out);
}

// aten::int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & int_repr_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::int_repr_out::call(self, out);
}
// aten::int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & int_repr_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::int_repr_out::call(self, out);
}

// aten::_make_per_tensor_quantized_tensor.out(Tensor self, float scale, int zero_point, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _make_per_tensor_quantized_tensor_out(at::Tensor & out, const at::Tensor & self, double scale, int64_t zero_point) {
    return at::_ops::_make_per_tensor_quantized_tensor_out::call(self, scale, zero_point, out);
}
// aten::_make_per_tensor_quantized_tensor.out(Tensor self, float scale, int zero_point, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _make_per_tensor_quantized_tensor_outf(const at::Tensor & self, double scale, int64_t zero_point, at::Tensor & out) {
    return at::_ops::_make_per_tensor_quantized_tensor_out::call(self, scale, zero_point, out);
}

// aten::_make_per_channel_quantized_tensor.out(Tensor self, Tensor scale, Tensor zero_point, int axis, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _make_per_channel_quantized_tensor_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis) {
    return at::_ops::_make_per_channel_quantized_tensor_out::call(self, scale, zero_point, axis, out);
}
// aten::_make_per_channel_quantized_tensor.out(Tensor self, Tensor scale, Tensor zero_point, int axis, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _make_per_channel_quantized_tensor_outf(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, at::Tensor & out) {
    return at::_ops::_make_per_channel_quantized_tensor_out::call(self, scale, zero_point, axis, out);
}

// aten::fake_quantize_per_tensor_affine_cachemask.out(Tensor self, float scale, int zero_point, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> fake_quantize_per_tensor_affine_cachemask_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) {
    return at::_ops::fake_quantize_per_tensor_affine_cachemask_out::call(self, scale, zero_point, quant_min, quant_max, out0, out1);
}
// aten::fake_quantize_per_tensor_affine_cachemask.out(Tensor self, float scale, int zero_point, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> fake_quantize_per_tensor_affine_cachemask_outf(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::fake_quantize_per_tensor_affine_cachemask_out::call(self, scale, zero_point, quant_min, quant_max, out0, out1);
}

// aten::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams.out(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max) {
    return at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out::call(self, scale, zero_point, fake_quant_enabled, quant_min, quant_max, out0, out1);
}
// aten::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams.out(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_outf(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out::call(self, scale, zero_point, fake_quant_enabled, quant_min, quant_max, out0, out1);
}

// aten::_fake_quantize_learnable_per_tensor_affine.out(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fake_quantize_learnable_per_tensor_affine_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) {
    return at::_ops::_fake_quantize_learnable_per_tensor_affine_out::call(self, scale, zero_point, quant_min, quant_max, grad_factor, out);
}
// aten::_fake_quantize_learnable_per_tensor_affine.out(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fake_quantize_learnable_per_tensor_affine_outf(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor, at::Tensor & out) {
    return at::_ops::_fake_quantize_learnable_per_tensor_affine_out::call(self, scale, zero_point, quant_min, quant_max, grad_factor, out);
}

// aten::fake_quantize_per_channel_affine_cachemask.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> fake_quantize_per_channel_affine_cachemask_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) {
    return at::_ops::fake_quantize_per_channel_affine_cachemask_out::call(self, scale, zero_point, axis, quant_min, quant_max, out0, out1);
}
// aten::fake_quantize_per_channel_affine_cachemask.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> fake_quantize_per_channel_affine_cachemask_outf(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::fake_quantize_per_channel_affine_cachemask_out::call(self, scale, zero_point, axis, quant_min, quant_max, out0, out1);
}

// aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fake_quantize_learnable_per_channel_affine_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) {
    return at::_ops::_fake_quantize_learnable_per_channel_affine_out::call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor, out);
}
// aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fake_quantize_learnable_per_channel_affine_outf(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor, at::Tensor & out) {
    return at::_ops::_fake_quantize_learnable_per_channel_affine_out::call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor, out);
}

// aten::_fused_moving_avg_obs_fq_helper.out(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False, *, Tensor(e!) out0, Tensor(f!) out1) -> (Tensor(e!), Tensor(f!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _fused_moving_avg_obs_fq_helper_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=false, bool symmetric_quant=false) {
    return at::_ops::_fused_moving_avg_obs_fq_helper_out::call(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant, out0, out1);
}
// aten::_fused_moving_avg_obs_fq_helper.out(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False, *, Tensor(e!) out0, Tensor(f!) out1) -> (Tensor(e!), Tensor(f!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _fused_moving_avg_obs_fq_helper_outf(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_fused_moving_avg_obs_fq_helper_out::call(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant, out0, out1);
}

// aten::_fused_moving_avg_obs_fq_helper_functional(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor running_min, Tensor running_max, Tensor scale, Tensor zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask, Tensor running_min_out, Tensor running_max_out, Tensor scale_out, Tensor zero_point_out)
inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _fused_moving_avg_obs_fq_helper_functional(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, const at::Tensor & running_min, const at::Tensor & running_max, const at::Tensor & scale, const at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=false, bool symmetric_quant=false) {
    return at::_ops::_fused_moving_avg_obs_fq_helper_functional::call(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant);
}

// aten::_to_copy.out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_copy_out(at::Tensor & out, const at::Tensor & self, bool non_blocking=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
    return at::_ops::_to_copy_out::call(self, non_blocking, memory_format, out);
}
// aten::_to_copy.out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _to_copy_outf(const at::Tensor & self, bool non_blocking, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
    return at::_ops::_to_copy_out::call(self, non_blocking, memory_format, out);
}

// aten::_lstm_mps.out(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _lstm_mps_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
    return at::_ops::_lstm_mps_out::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2, out3, out4, out5);
}
// aten::_lstm_mps.out(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _lstm_mps_outf(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5) {
    return at::_ops::_lstm_mps_out::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2, out3, out4, out5);
}

// aten::lstm_mps_backward.out(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> ()
inline void lstm_mps_backward_out(at::Tensor & out0, at::TensorList out1, at::TensorList out2, const ::std::optional<at::Tensor> & grad_y, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) {
    return at::_ops::lstm_mps_backward_out::call(grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, layersOutputs, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2);
}
// aten::lstm_mps_backward.out(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> ()
inline void lstm_mps_backward_outf(const ::std::optional<at::Tensor> & grad_y, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::TensorList out1, at::TensorList out2) {
    return at::_ops::lstm_mps_backward_out::call(grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, layersOutputs, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2);
}

// aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const ::std::optional<at::Tensor> & input_bias={}, const ::std::optional<at::Tensor> & hidden_bias={}) {
    return at::_ops::_thnn_fused_lstm_cell_out::call(input_gates, hidden_gates, cx, input_bias, hidden_bias, out0, out1, out2);
}
// aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_outf(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_thnn_fused_lstm_cell_out::call(input_gates, hidden_gates, cx, input_bias, hidden_bias, out0, out1, out2);
}

// aten::_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_backward_impl_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) {
    return at::_ops::_thnn_fused_lstm_cell_backward_impl_out::call(grad_hy, grad_cy, cx, cy, workspace, has_bias, out0, out1, out2);
}
// aten::_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_lstm_cell_backward_impl_outf(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_thnn_fused_lstm_cell_backward_impl_out::call(grad_hy, grad_cy, cx, cy, workspace, has_bias, out0, out1, out2);
}

// aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const ::std::optional<at::Tensor> & input_bias={}, const ::std::optional<at::Tensor> & hidden_bias={}) {
    return at::_ops::_thnn_fused_gru_cell_out::call(input_gates, hidden_gates, hx, input_bias, hidden_bias, out0, out1);
}
// aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_outf(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const ::std::optional<at::Tensor> & input_bias, const ::std::optional<at::Tensor> & hidden_bias, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_thnn_fused_gru_cell_out::call(input_gates, hidden_gates, hx, input_bias, hidden_bias, out0, out1);
}

// aten::_thnn_fused_gru_cell_backward.out(Tensor grad_hy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias) {
    return at::_ops::_thnn_fused_gru_cell_backward_out::call(grad_hy, workspace, has_bias, out0, out1, out2, out3, out4);
}
// aten::_thnn_fused_gru_cell_backward.out(Tensor grad_hy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_backward_outf(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) {
    return at::_ops::_thnn_fused_gru_cell_backward_out::call(grad_hy, workspace, has_bias, out0, out1, out2, out3, out4);
}

// aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _pack_padded_sequence_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const at::Tensor & lengths, bool batch_first) {
    return at::_ops::_pack_padded_sequence_out::call(input, lengths, batch_first, out0, out1);
}
// aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _pack_padded_sequence_outf(const at::Tensor & input, const at::Tensor & lengths, bool batch_first, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_pack_padded_sequence_out::call(input, lengths, batch_first, out0, out1);
}

// aten::set.source_Storage_out(Tensor self, Storage source, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, at::Storage source) {
    return at::_ops::set_source_Storage_out::call(self, source, out);
}
// aten::set.source_Storage_out(Tensor self, Storage source, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_outf(const at::Tensor & self, at::Storage source, at::Tensor & out) {
    return at::_ops::set_source_Storage_out::call(self, source, out);
}

// aten::set.source_Storage(Tensor self, Storage source) -> Tensor
inline at::Tensor set(const at::Tensor & self, at::Storage source) {
    return at::_ops::set_source_Storage::call(self, source);
}

// aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_outf(const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & set_outf(const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_symint_out(at::Tensor & out, const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, size, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, size, stride, out);
  }
}

// aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_symint_outf(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, size, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & set_outf(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, size, stride, out);
  }
}

// aten::set.source_Storage_storage_offset(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor
inline at::Tensor set(const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
    return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor set(const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
    return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
  }
}

// aten::set.source_Storage_storage_offset(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor
inline at::Tensor set_symint(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
    return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, size, stride);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor set(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
    return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, size, stride);
  }
}

// aten::set.source_Tensor_out(Tensor self, Tensor source, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & source) {
    return at::_ops::set_source_Tensor_out::call(self, source, out);
}
// aten::set.source_Tensor_out(Tensor self, Tensor source, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_outf(const at::Tensor & self, const at::Tensor & source, at::Tensor & out) {
    return at::_ops::set_source_Tensor_out::call(self, source, out);
}

// aten::set.source_Tensor(Tensor self, Tensor source) -> Tensor
inline at::Tensor set(const at::Tensor & self, const at::Tensor & source) {
    return at::_ops::set_source_Tensor::call(self, source);
}

// aten::set.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::set_out::call(self, out);
}
// aten::set.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & set_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::set_out::call(self, out);
}

// aten::set(Tensor self) -> Tensor
inline at::Tensor set(const at::Tensor & self) {
    return at::_ops::set::call(self);
}

// aten::lift.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lift_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::lift_out::call(self, out);
}
// aten::lift.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lift_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::lift_out::call(self, out);
}

// aten::lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lift_fresh_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::lift_fresh_copy_out::call(self, out);
}
// aten::lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & lift_fresh_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::lift_fresh_copy_out::call(self, out);
}

// aten::masked_fill.Scalar_out(Tensor self, Tensor mask, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & masked_fill_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) {
    return at::_ops::masked_fill_Scalar_out::call(self, mask, value, out);
}
// aten::masked_fill.Scalar_out(Tensor self, Tensor mask, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & masked_fill_outf(const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::masked_fill_Scalar_out::call(self, mask, value, out);
}

// aten::masked_fill.Tensor_out(Tensor self, Tensor mask, Tensor value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & masked_fill_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) {
    return at::_ops::masked_fill_Tensor_out::call(self, mask, value, out);
}
// aten::masked_fill.Tensor_out(Tensor self, Tensor mask, Tensor value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & masked_fill_outf(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value, at::Tensor & out) {
    return at::_ops::masked_fill_Tensor_out::call(self, mask, value, out);
}

// aten::masked_scatter.out(Tensor self, Tensor mask, Tensor source, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & masked_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) {
    return at::_ops::masked_scatter_out::call(self, mask, source, out);
}
// aten::masked_scatter.out(Tensor self, Tensor mask, Tensor source, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & masked_scatter_outf(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source, at::Tensor & out) {
    return at::_ops::masked_scatter_out::call(self, mask, source, out);
}

// aten::_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _masked_softmax_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, ::std::optional<int64_t> dim=::std::nullopt, ::std::optional<int64_t> mask_type=::std::nullopt) {
    return at::_ops::_masked_softmax_out::call(self, mask, dim, mask_type, out);
}
// aten::_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _masked_softmax_outf(const at::Tensor & self, const at::Tensor & mask, ::std::optional<int64_t> dim, ::std::optional<int64_t> mask_type, at::Tensor & out) {
    return at::_ops::_masked_softmax_out::call(self, mask, dim, mask_type, out);
}

// aten::_masked_softmax_backward.out(Tensor grad_output, Tensor output, Tensor mask, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _masked_softmax_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, ::std::optional<int64_t> dim=::std::nullopt) {
    return at::_ops::_masked_softmax_backward_out::call(grad_output, output, mask, dim, out);
}
// aten::_masked_softmax_backward.out(Tensor grad_output, Tensor output, Tensor mask, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _masked_softmax_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, ::std::optional<int64_t> dim, at::Tensor & out) {
    return at::_ops::_masked_softmax_backward_out::call(grad_output, output, mask, dim, out);
}

// aten::put.out(Tensor self, Tensor index, Tensor source, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & put_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate=false) {
    return at::_ops::put_out::call(self, index, source, accumulate, out);
}
// aten::put.out(Tensor self, Tensor index, Tensor source, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & put_outf(const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate, at::Tensor & out) {
    return at::_ops::put_out::call(self, index, source, accumulate, out);
}

// aten::index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_fill_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) {
    return at::_ops::index_fill_int_Scalar_out::call(self, dim, index, value, out);
}
// aten::index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_fill_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) {
    return at::_ops::index_fill_int_Scalar_out::call(self, dim, index, value, out);
}

// aten::index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_fill_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) {
    return at::_ops::index_fill_int_Tensor_out::call(self, dim, index, value, out);
}
// aten::index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & index_fill_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value, at::Tensor & out) {
    return at::_ops::index_fill_int_Tensor_out::call(self, dim, index, value, out);
}

// aten::bitwise_and.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_and_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_and_Scalar_Tensor_out::call(self, other, out);
}
// aten::bitwise_and.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_and_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_and_Scalar_Tensor_out::call(self, other, out);
}

// aten::bitwise_or.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_or_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_or_Scalar_Tensor_out::call(self, other, out);
}
// aten::bitwise_or.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_or_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_or_Scalar_Tensor_out::call(self, other, out);
}

// aten::bitwise_xor.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_xor_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_xor_Scalar_Tensor_out::call(self, other, out);
}
// aten::bitwise_xor.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_xor_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_xor_Scalar_Tensor_out::call(self, other, out);
}

// aten::__lshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & __lshift___out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::__lshift___Scalar_out::call(self, other, out);
}
// aten::__lshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & __lshift___outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::__lshift___Scalar_out::call(self, other, out);
}

// aten::__lshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & __lshift___out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::__lshift___Tensor_out::call(self, other, out);
}
// aten::__lshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & __lshift___outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::__lshift___Tensor_out::call(self, other, out);
}

// aten::bitwise_left_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_left_shift_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_left_shift_Scalar_Tensor_out::call(self, other, out);
}
// aten::bitwise_left_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_left_shift_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_left_shift_Scalar_Tensor_out::call(self, other, out);
}

// aten::__rshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & __rshift___out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
    return at::_ops::__rshift___Scalar_out::call(self, other, out);
}
// aten::__rshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & __rshift___outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
    return at::_ops::__rshift___Scalar_out::call(self, other, out);
}

// aten::__rshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & __rshift___out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
    return at::_ops::__rshift___Tensor_out::call(self, other, out);
}
// aten::__rshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & __rshift___outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::__rshift___Tensor_out::call(self, other, out);
}

// aten::bitwise_right_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_right_shift_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::bitwise_right_shift_Scalar_Tensor_out::call(self, other, out);
}
// aten::bitwise_right_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bitwise_right_shift_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::bitwise_right_shift_Scalar_Tensor_out::call(self, other, out);
}

// aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::random_from_out::call(self, from, to, generator, out);
}
// aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & random_outf(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::random_from_out::call(self, from, to, generator, out);
}

// aten::random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor
inline at::Tensor random(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::random_from::call(self, from, to, generator);
}

// aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::random_to_out::call(self, to, generator, out);
}
// aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & random_outf(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::random_to_out::call(self, to, generator, out);
}

// aten::random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor
inline at::Tensor random(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::random_to::call(self, to, generator);
}

// aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::random_out::call(self, generator, out);
}
// aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & random_outf(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::random_out::call(self, generator, out);
}

// aten::random(Tensor self, *, Generator? generator=None) -> Tensor
inline at::Tensor random(const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::random::call(self, generator);
}

// aten::uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & uniform_out(at::Tensor & out, const at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::uniform_out::call(self, from, to, generator, out);
}
// aten::uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & uniform_outf(const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::uniform_out::call(self, from, to, generator, out);
}

// aten::uniform(Tensor self, float from=0, float to=1, *, Generator? generator=None) -> Tensor
inline at::Tensor uniform(const at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::uniform::call(self, from, to, generator);
}

// aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cauchy_out(at::Tensor & out, const at::Tensor & self, double median=0, double sigma=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::cauchy_out::call(self, median, sigma, generator, out);
}
// aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & cauchy_outf(const at::Tensor & self, double median, double sigma, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::cauchy_out::call(self, median, sigma, generator, out);
}

// aten::cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor
inline at::Tensor cauchy(const at::Tensor & self, double median=0, double sigma=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::cauchy::call(self, median, sigma, generator);
}

// aten::log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log_normal_out(at::Tensor & out, const at::Tensor & self, double mean=1, double std=2, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::log_normal_out::call(self, mean, std, generator, out);
}
// aten::log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & log_normal_outf(const at::Tensor & self, double mean, double std, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::log_normal_out::call(self, mean, std, generator, out);
}

// aten::log_normal(Tensor self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor
inline at::Tensor log_normal(const at::Tensor & self, double mean=1, double std=2, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::log_normal::call(self, mean, std, generator);
}

// aten::exponential.out(Tensor self, float lambd=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & exponential_out(at::Tensor & out, const at::Tensor & self, double lambd=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::exponential_out::call(self, lambd, generator, out);
}
// aten::exponential.out(Tensor self, float lambd=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & exponential_outf(const at::Tensor & self, double lambd, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::exponential_out::call(self, lambd, generator, out);
}

// aten::exponential(Tensor self, float lambd=1, *, Generator? generator=None) -> Tensor
inline at::Tensor exponential(const at::Tensor & self, double lambd=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::exponential::call(self, lambd, generator);
}

// aten::geometric.out(Tensor self, float p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & geometric_out(at::Tensor & out, const at::Tensor & self, double p, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::geometric_out::call(self, p, generator, out);
}
// aten::geometric.out(Tensor self, float p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & geometric_outf(const at::Tensor & self, double p, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::geometric_out::call(self, p, generator, out);
}

// aten::geometric(Tensor self, float p, *, Generator? generator=None) -> Tensor
inline at::Tensor geometric(const at::Tensor & self, double p, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::geometric::call(self, p, generator);
}

// aten::tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tril_indices_out(at::Tensor & out, int64_t row, int64_t col, int64_t offset=0) {
    return at::_ops::tril_indices_out::call(row, col, offset, out);
}
// aten::tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & tril_indices_outf(int64_t row, int64_t col, int64_t offset, at::Tensor & out) {
    return at::_ops::tril_indices_out::call(row, col, offset, out);
}

// aten::triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & triu_indices_out(at::Tensor & out, int64_t row, int64_t col, int64_t offset=0) {
    return at::_ops::triu_indices_out::call(row, col, offset, out);
}
// aten::triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & triu_indices_outf(int64_t row, int64_t col, int64_t offset, at::Tensor & out) {
    return at::_ops::triu_indices_out::call(row, col, offset, out);
}

// aten::trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & trace_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::trace_out::call(self, out);
}
// aten::trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & trace_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::trace_out::call(self, out);
}

// aten::_cholesky_solve_helper.out(Tensor self, Tensor A, bool upper, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cholesky_solve_helper_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & A, bool upper) {
    return at::_ops::_cholesky_solve_helper_out::call(self, A, upper, out);
}
// aten::_cholesky_solve_helper.out(Tensor self, Tensor A, bool upper, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _cholesky_solve_helper_outf(const at::Tensor & self, const at::Tensor & A, bool upper, at::Tensor & out) {
    return at::_ops::_cholesky_solve_helper_out::call(self, A, upper, out);
}

// aten::dist.out(Tensor self, Tensor other, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & dist_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & p=2) {
    return at::_ops::dist_out::call(self, other, p, out);
}
// aten::dist.out(Tensor self, Tensor other, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & dist_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & p, at::Tensor & out) {
    return at::_ops::dist_out::call(self, other, p, out);
}

// aten::_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> ()
inline void _histogramdd_bin_edges_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::_histogramdd_bin_edges_out::call(self, bins, range, weight, density, out);
}
// aten::_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> ()
inline void _histogramdd_bin_edges_outf(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density, at::TensorList out) {
    return at::_ops::_histogramdd_bin_edges_out::call(self, bins, range, weight, density, out);
}

// aten::_histogramdd_from_bin_cts.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _histogramdd_from_bin_cts_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range=::std::nullopt, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::_histogramdd_from_bin_cts_out::call(self, bins, range, weight, density, out);
}
// aten::_histogramdd_from_bin_cts.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _histogramdd_from_bin_cts_outf(const at::Tensor & self, at::IntArrayRef bins, ::std::optional<at::ArrayRef<double>> range, const ::std::optional<at::Tensor> & weight, bool density, at::Tensor & out) {
    return at::_ops::_histogramdd_from_bin_cts_out::call(self, bins, range, weight, density, out);
}

// aten::_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _histogramdd_from_bin_tensors_out(at::Tensor & out, const at::Tensor & self, at::TensorList bins, const ::std::optional<at::Tensor> & weight={}, bool density=false) {
    return at::_ops::_histogramdd_from_bin_tensors_out::call(self, bins, weight, density, out);
}
// aten::_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _histogramdd_from_bin_tensors_outf(const at::Tensor & self, at::TensorList bins, const ::std::optional<at::Tensor> & weight, bool density, at::Tensor & out) {
    return at::_ops::_histogramdd_from_bin_tensors_out::call(self, bins, weight, density, out);
}

// aten::remainder.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & remainder_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
    return at::_ops::remainder_Scalar_Tensor_out::call(self, other, out);
}
// aten::remainder.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & remainder_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
    return at::_ops::remainder_Scalar_Tensor_out::call(self, other, out);
}

// aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & unfold_backward_out(at::Tensor & out, const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
    return at::_ops::unfold_backward_out::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & unfold_backward_out(at::Tensor & out, const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
    return at::_ops::unfold_backward_out::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out);
  }
}

// aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & unfold_backward_outf(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) {
    return at::_ops::unfold_backward_out::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & unfold_backward_outf(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) {
    return at::_ops::unfold_backward_out::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out);
  }
}

// aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & unfold_backward_symint_out(at::Tensor & out, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
    return at::_ops::unfold_backward_out::call(grad_in, input_sizes, dim, size, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & unfold_backward_out(at::Tensor & out, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
    return at::_ops::unfold_backward_out::call(grad_in, input_sizes, dim, size, step, out);
  }
}

// aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & unfold_backward_symint_outf(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) {
    return at::_ops::unfold_backward_out::call(grad_in, input_sizes, dim, size, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & unfold_backward_outf(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) {
    return at::_ops::unfold_backward_out::call(grad_in, input_sizes, dim, size, step, out);
  }
}

// aten::normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_out(at::Tensor & out, const at::Tensor & self, double mean=0, double std=1, ::std::optional<at::Generator> generator=::std::nullopt) {
    return at::_ops::normal_out::call(self, mean, std, generator, out);
}
// aten::normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & normal_outf(const at::Tensor & self, double mean, double std, ::std::optional<at::Generator> generator, at::Tensor & out) {
    return at::_ops::normal_out::call(self, mean, std, generator, out);
}

// aten::_amp_foreach_non_finite_check_and_unscale.out(Tensor[] self, Tensor(b!) found_inf, Tensor inv_scale, *, Tensor(a!)[] out) -> ()
inline void _amp_foreach_non_finite_check_and_unscale_out(at::TensorList out, at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale) {
    return at::_ops::_amp_foreach_non_finite_check_and_unscale_out::call(self, found_inf, inv_scale, out);
}
// aten::_amp_foreach_non_finite_check_and_unscale.out(Tensor[] self, Tensor(b!) found_inf, Tensor inv_scale, *, Tensor(a!)[] out) -> ()
inline void _amp_foreach_non_finite_check_and_unscale_outf(at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale, at::TensorList out) {
    return at::_ops::_amp_foreach_non_finite_check_and_unscale_out::call(self, found_inf, inv_scale, out);
}

// aten::_amp_foreach_non_finite_check_and_unscale(Tensor[] self, Tensor found_inf, Tensor inv_scale) -> (Tensor[] self_out, Tensor found_inf_out)
inline ::std::tuple<::std::vector<at::Tensor>,at::Tensor> _amp_foreach_non_finite_check_and_unscale(at::TensorList self, const at::Tensor & found_inf, const at::Tensor & inv_scale) {
    return at::_ops::_amp_foreach_non_finite_check_and_unscale::call(self, found_inf, inv_scale);
}

// aten::_amp_update_scale.out(Tensor self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _amp_update_scale_out(at::Tensor & out, const at::Tensor & self, at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) {
    return at::_ops::_amp_update_scale_out::call(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval, out);
}
// aten::_amp_update_scale.out(Tensor self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _amp_update_scale_outf(const at::Tensor & self, at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval, at::Tensor & out) {
    return at::_ops::_amp_update_scale_out::call(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval, out);
}

// aten::_amp_update_scale(Tensor self, Tensor growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> (Tensor, Tensor growth_tracker_out)
inline ::std::tuple<at::Tensor,at::Tensor> _amp_update_scale(const at::Tensor & self, const at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) {
    return at::_ops::_amp_update_scale::call(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval);
}

// aten::_foreach_add.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_add_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_add_Scalar_out::call(self, scalar, out);
}
// aten::_foreach_add.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_add_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
    return at::_ops::_foreach_add_Scalar_out::call(self, scalar, out);
}

// aten::_foreach_add.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
inline void _foreach_add_out(at::TensorList out, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_add_List_out::call(self, other, alpha, out);
}
// aten::_foreach_add.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
inline void _foreach_add_outf(at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) {
    return at::_ops::_foreach_add_List_out::call(self, other, alpha, out);
}

// aten::_foreach_add.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_add_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_add_ScalarList_out::call(self, scalars, out);
}
// aten::_foreach_add.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_add_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_add_ScalarList_out::call(self, scalars, out);
}

// aten::_foreach_add.Tensor_out(Tensor[] self, Tensor other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
inline void _foreach_add_out(at::TensorList out, at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_add_Tensor_out::call(self, other, alpha, out);
}
// aten::_foreach_add.Tensor_out(Tensor[] self, Tensor other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
inline void _foreach_add_outf(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha, at::TensorList out) {
    return at::_ops::_foreach_add_Tensor_out::call(self, other, alpha, out);
}

// aten::_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_sub_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_sub_Scalar_out::call(self, scalar, out);
}
// aten::_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_sub_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
    return at::_ops::_foreach_sub_Scalar_out::call(self, scalar, out);
}

// aten::_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
inline void _foreach_sub_out(at::TensorList out, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) {
    return at::_ops::_foreach_sub_List_out::call(self, other, alpha, out);
}
// aten::_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> ()
inline void _foreach_sub_outf(at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) {
    return at::_ops::_foreach_sub_List_out::call(self, other, alpha, out);
}

// aten::_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_sub_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_sub_ScalarList_out::call(self, scalars, out);
}
// aten::_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_sub_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_sub_ScalarList_out::call(self, scalars, out);
}

// aten::_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_mul_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_mul_Scalar_out::call(self, scalar, out);
}
// aten::_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_mul_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
    return at::_ops::_foreach_mul_Scalar_out::call(self, scalar, out);
}

// aten::_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_mul_out(at::TensorList out, at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_mul_List_out::call(self, other, out);
}
// aten::_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_mul_outf(at::TensorList self, at::TensorList other, at::TensorList out) {
    return at::_ops::_foreach_mul_List_out::call(self, other, out);
}

// aten::_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_mul_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_mul_ScalarList_out::call(self, scalars, out);
}
// aten::_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_mul_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_mul_ScalarList_out::call(self, scalars, out);
}

// aten::_foreach_mul.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> ()
inline void _foreach_mul_out(at::TensorList out, at::TensorList self, const at::Tensor & other) {
    return at::_ops::_foreach_mul_Tensor_out::call(self, other, out);
}
// aten::_foreach_mul.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> ()
inline void _foreach_mul_outf(at::TensorList self, const at::Tensor & other, at::TensorList out) {
    return at::_ops::_foreach_mul_Tensor_out::call(self, other, out);
}

// aten::_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_div_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_div_Scalar_out::call(self, scalar, out);
}
// aten::_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_div_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
    return at::_ops::_foreach_div_Scalar_out::call(self, scalar, out);
}

// aten::_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_div_out(at::TensorList out, at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_div_List_out::call(self, other, out);
}
// aten::_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_div_outf(at::TensorList self, at::TensorList other, at::TensorList out) {
    return at::_ops::_foreach_div_List_out::call(self, other, out);
}

// aten::_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_div_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_div_ScalarList_out::call(self, scalars, out);
}
// aten::_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_div_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_div_ScalarList_out::call(self, scalars, out);
}

// aten::_foreach_div.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> ()
inline void _foreach_div_out(at::TensorList out, at::TensorList self, const at::Tensor & other) {
    return at::_ops::_foreach_div_Tensor_out::call(self, other, out);
}
// aten::_foreach_div.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> ()
inline void _foreach_div_outf(at::TensorList self, const at::Tensor & other, at::TensorList out) {
    return at::_ops::_foreach_div_Tensor_out::call(self, other, out);
}

// aten::_foreach_clamp_max.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_max_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_clamp_max_Scalar_out::call(self, scalar, out);
}
// aten::_foreach_clamp_max.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_max_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
    return at::_ops::_foreach_clamp_max_Scalar_out::call(self, scalar, out);
}

// aten::_foreach_clamp_max.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_max_out(at::TensorList out, at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_clamp_max_List_out::call(self, other, out);
}
// aten::_foreach_clamp_max.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_max_outf(at::TensorList self, at::TensorList other, at::TensorList out) {
    return at::_ops::_foreach_clamp_max_List_out::call(self, other, out);
}

// aten::_foreach_clamp_max.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_max_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_clamp_max_ScalarList_out::call(self, scalars, out);
}
// aten::_foreach_clamp_max.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_max_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_clamp_max_ScalarList_out::call(self, scalars, out);
}

// aten::_foreach_clamp_min.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_min_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_clamp_min_Scalar_out::call(self, scalar, out);
}
// aten::_foreach_clamp_min.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_min_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
    return at::_ops::_foreach_clamp_min_Scalar_out::call(self, scalar, out);
}

// aten::_foreach_clamp_min.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_min_out(at::TensorList out, at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_clamp_min_List_out::call(self, other, out);
}
// aten::_foreach_clamp_min.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_min_outf(at::TensorList self, at::TensorList other, at::TensorList out) {
    return at::_ops::_foreach_clamp_min_List_out::call(self, other, out);
}

// aten::_foreach_clamp_min.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_min_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_clamp_min_ScalarList_out::call(self, scalars, out);
}
// aten::_foreach_clamp_min.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_clamp_min_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_clamp_min_ScalarList_out::call(self, scalars, out);
}

// aten::_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_maximum_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_maximum_Scalar_out::call(self, scalar, out);
}
// aten::_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_maximum_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
    return at::_ops::_foreach_maximum_Scalar_out::call(self, scalar, out);
}

// aten::_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_maximum_out(at::TensorList out, at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_maximum_List_out::call(self, other, out);
}
// aten::_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_maximum_outf(at::TensorList self, at::TensorList other, at::TensorList out) {
    return at::_ops::_foreach_maximum_List_out::call(self, other, out);
}

// aten::_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_maximum_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_maximum_ScalarList_out::call(self, scalars, out);
}
// aten::_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_maximum_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_maximum_ScalarList_out::call(self, scalars, out);
}

// aten::_foreach_minimum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_minimum_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
    return at::_ops::_foreach_minimum_Scalar_out::call(self, scalar, out);
}
// aten::_foreach_minimum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
inline void _foreach_minimum_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
    return at::_ops::_foreach_minimum_Scalar_out::call(self, scalar, out);
}

// aten::_foreach_minimum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_minimum_out(at::TensorList out, at::TensorList self, at::TensorList other) {
    return at::_ops::_foreach_minimum_List_out::call(self, other, out);
}
// aten::_foreach_minimum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
inline void _foreach_minimum_outf(at::TensorList self, at::TensorList other, at::TensorList out) {
    return at::_ops::_foreach_minimum_List_out::call(self, other, out);
}

// aten::_foreach_minimum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_minimum_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_minimum_ScalarList_out::call(self, scalars, out);
}
// aten::_foreach_minimum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_minimum_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_minimum_ScalarList_out::call(self, scalars, out);
}

// aten::_foreach_addcdiv.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcdiv_out(at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) {
    return at::_ops::_foreach_addcdiv_Scalar_out::call(self, tensor1, tensor2, value, out);
}
// aten::_foreach_addcdiv.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcdiv_outf(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out) {
    return at::_ops::_foreach_addcdiv_Scalar_out::call(self, tensor1, tensor2, value, out);
}

// aten::_foreach_addcdiv.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcdiv_out(at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_addcdiv_ScalarList_out::call(self, tensor1, tensor2, scalars, out);
}
// aten::_foreach_addcdiv.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcdiv_outf(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_addcdiv_ScalarList_out::call(self, tensor1, tensor2, scalars, out);
}

// aten::_foreach_addcdiv.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcdiv_out(at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) {
    return at::_ops::_foreach_addcdiv_Tensor_out::call(self, tensor1, tensor2, scalars, out);
}
// aten::_foreach_addcdiv.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcdiv_outf(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out) {
    return at::_ops::_foreach_addcdiv_Tensor_out::call(self, tensor1, tensor2, scalars, out);
}

// aten::_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcmul_out(at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) {
    return at::_ops::_foreach_addcmul_Scalar_out::call(self, tensor1, tensor2, value, out);
}
// aten::_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcmul_outf(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out) {
    return at::_ops::_foreach_addcmul_Scalar_out::call(self, tensor1, tensor2, value, out);
}

// aten::_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcmul_out(at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars) {
    return at::_ops::_foreach_addcmul_ScalarList_out::call(self, tensor1, tensor2, scalars, out);
}
// aten::_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcmul_outf(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
    return at::_ops::_foreach_addcmul_ScalarList_out::call(self, tensor1, tensor2, scalars, out);
}

// aten::_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcmul_out(at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) {
    return at::_ops::_foreach_addcmul_Tensor_out::call(self, tensor1, tensor2, scalars, out);
}
// aten::_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()
inline void _foreach_addcmul_outf(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out) {
    return at::_ops::_foreach_addcmul_Tensor_out::call(self, tensor1, tensor2, scalars, out);
}

// aten::_foreach_abs.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_abs_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_abs_out::call(self, out);
}
// aten::_foreach_abs.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_abs_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_abs_out::call(self, out);
}

// aten::_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_acos_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_acos_out::call(self, out);
}
// aten::_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_acos_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_acos_out::call(self, out);
}

// aten::_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_asin_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_asin_out::call(self, out);
}
// aten::_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_asin_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_asin_out::call(self, out);
}

// aten::_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_atan_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_atan_out::call(self, out);
}
// aten::_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_atan_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_atan_out::call(self, out);
}

// aten::_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_ceil_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_ceil_out::call(self, out);
}
// aten::_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_ceil_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_ceil_out::call(self, out);
}

// aten::_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_cos_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_cos_out::call(self, out);
}
// aten::_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_cos_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_cos_out::call(self, out);
}

// aten::_foreach_cosh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_cosh_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_cosh_out::call(self, out);
}
// aten::_foreach_cosh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_cosh_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_cosh_out::call(self, out);
}

// aten::_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_erf_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_erf_out::call(self, out);
}
// aten::_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_erf_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_erf_out::call(self, out);
}

// aten::_foreach_erfc.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_erfc_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_erfc_out::call(self, out);
}
// aten::_foreach_erfc.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_erfc_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_erfc_out::call(self, out);
}

// aten::_foreach_exp.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_exp_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_exp_out::call(self, out);
}
// aten::_foreach_exp.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_exp_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_exp_out::call(self, out);
}

// aten::_foreach_expm1.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_expm1_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_expm1_out::call(self, out);
}
// aten::_foreach_expm1.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_expm1_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_expm1_out::call(self, out);
}

// aten::_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_floor_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_floor_out::call(self, out);
}
// aten::_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_floor_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_floor_out::call(self, out);
}

// aten::_foreach_frac.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_frac_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_frac_out::call(self, out);
}
// aten::_foreach_frac.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_frac_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_frac_out::call(self, out);
}

// aten::_foreach_lerp.List_out(Tensor[] self, Tensor[] tensors1, Tensor[] weights, *, Tensor(a!)[] out) -> ()
inline void _foreach_lerp_out(at::TensorList out, at::TensorList self, at::TensorList tensors1, at::TensorList weights) {
    return at::_ops::_foreach_lerp_List_out::call(self, tensors1, weights, out);
}
// aten::_foreach_lerp.List_out(Tensor[] self, Tensor[] tensors1, Tensor[] weights, *, Tensor(a!)[] out) -> ()
inline void _foreach_lerp_outf(at::TensorList self, at::TensorList tensors1, at::TensorList weights, at::TensorList out) {
    return at::_ops::_foreach_lerp_List_out::call(self, tensors1, weights, out);
}

// aten::_foreach_lerp.Scalar_out(Tensor[] self, Tensor[] tensors1, Scalar weight, *, Tensor(a!)[] out) -> ()
inline void _foreach_lerp_out(at::TensorList out, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) {
    return at::_ops::_foreach_lerp_Scalar_out::call(self, tensors1, weight, out);
}
// aten::_foreach_lerp.Scalar_out(Tensor[] self, Tensor[] tensors1, Scalar weight, *, Tensor(a!)[] out) -> ()
inline void _foreach_lerp_outf(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight, at::TensorList out) {
    return at::_ops::_foreach_lerp_Scalar_out::call(self, tensors1, weight, out);
}

// aten::_foreach_lerp.ScalarList_out(Tensor[] self, Tensor[] tensors1, Scalar[] weight, *, Tensor(a!)[] out) -> ()
inline void _foreach_lerp_out(at::TensorList out, at::TensorList self, at::TensorList tensors1, at::ArrayRef<at::Scalar> weight) {
    return at::_ops::_foreach_lerp_ScalarList_out::call(self, tensors1, weight, out);
}
// aten::_foreach_lerp.ScalarList_out(Tensor[] self, Tensor[] tensors1, Scalar[] weight, *, Tensor(a!)[] out) -> ()
inline void _foreach_lerp_outf(at::TensorList self, at::TensorList tensors1, at::ArrayRef<at::Scalar> weight, at::TensorList out) {
    return at::_ops::_foreach_lerp_ScalarList_out::call(self, tensors1, weight, out);
}

// aten::_foreach_lgamma.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_lgamma_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_lgamma_out::call(self, out);
}
// aten::_foreach_lgamma.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_lgamma_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_lgamma_out::call(self, out);
}

// aten::_foreach_log.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_log_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_log_out::call(self, out);
}
// aten::_foreach_log.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_log_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_log_out::call(self, out);
}

// aten::_foreach_log10.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_log10_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_log10_out::call(self, out);
}
// aten::_foreach_log10.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_log10_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_log10_out::call(self, out);
}

// aten::_foreach_log1p.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_log1p_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_log1p_out::call(self, out);
}
// aten::_foreach_log1p.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_log1p_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_log1p_out::call(self, out);
}

// aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_log2_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_log2_out::call(self, out);
}
// aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_log2_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_log2_out::call(self, out);
}

// aten::_foreach_max.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_max_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_max_out::call(self, out);
}
// aten::_foreach_max.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_max_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_max_out::call(self, out);
}

// aten::_foreach_neg.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_neg_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_neg_out::call(self, out);
}
// aten::_foreach_neg.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_neg_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_neg_out::call(self, out);
}

// aten::_foreach_norm.Scalar_out(Tensor[] self, Scalar ord=2, ScalarType? dtype=None, *, Tensor(a!)[] out) -> ()
inline void _foreach_norm_out(at::TensorList out, at::TensorList self, const at::Scalar & ord=2, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
    return at::_ops::_foreach_norm_Scalar_out::call(self, ord, dtype, out);
}
// aten::_foreach_norm.Scalar_out(Tensor[] self, Scalar ord=2, ScalarType? dtype=None, *, Tensor(a!)[] out) -> ()
inline void _foreach_norm_outf(at::TensorList self, const at::Scalar & ord, ::std::optional<at::ScalarType> dtype, at::TensorList out) {
    return at::_ops::_foreach_norm_Scalar_out::call(self, ord, dtype, out);
}

// aten::_foreach_pow.List_out(Tensor[] self, Tensor[] exponent, *, Tensor(a!)[] out) -> ()
inline void _foreach_pow_out(at::TensorList out, at::TensorList self, at::TensorList exponent) {
    return at::_ops::_foreach_pow_List_out::call(self, exponent, out);
}
// aten::_foreach_pow.List_out(Tensor[] self, Tensor[] exponent, *, Tensor(a!)[] out) -> ()
inline void _foreach_pow_outf(at::TensorList self, at::TensorList exponent, at::TensorList out) {
    return at::_ops::_foreach_pow_List_out::call(self, exponent, out);
}

// aten::_foreach_pow.Scalar_out(Tensor[] self, Scalar exponent, *, Tensor(a!)[] out) -> ()
inline void _foreach_pow_out(at::TensorList out, at::TensorList self, const at::Scalar & exponent) {
    return at::_ops::_foreach_pow_Scalar_out::call(self, exponent, out);
}
// aten::_foreach_pow.Scalar_out(Tensor[] self, Scalar exponent, *, Tensor(a!)[] out) -> ()
inline void _foreach_pow_outf(at::TensorList self, const at::Scalar & exponent, at::TensorList out) {
    return at::_ops::_foreach_pow_Scalar_out::call(self, exponent, out);
}

// aten::_foreach_pow.ScalarList_out(Tensor[] self, Scalar[] exponent, *, Tensor(a!)[] out) -> ()
inline void _foreach_pow_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> exponent) {
    return at::_ops::_foreach_pow_ScalarList_out::call(self, exponent, out);
}
// aten::_foreach_pow.ScalarList_out(Tensor[] self, Scalar[] exponent, *, Tensor(a!)[] out) -> ()
inline void _foreach_pow_outf(at::TensorList self, at::ArrayRef<at::Scalar> exponent, at::TensorList out) {
    return at::_ops::_foreach_pow_ScalarList_out::call(self, exponent, out);
}

// aten::_foreach_reciprocal.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_reciprocal_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_reciprocal_out::call(self, out);
}
// aten::_foreach_reciprocal.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_reciprocal_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_reciprocal_out::call(self, out);
}

// aten::_foreach_round.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_round_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_round_out::call(self, out);
}
// aten::_foreach_round.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_round_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_round_out::call(self, out);
}

// aten::_foreach_rsqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_rsqrt_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_rsqrt_out::call(self, out);
}
// aten::_foreach_rsqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_rsqrt_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_rsqrt_out::call(self, out);
}

// aten::_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sigmoid_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_sigmoid_out::call(self, out);
}
// aten::_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sigmoid_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_sigmoid_out::call(self, out);
}

// aten::_foreach_sign.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sign_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_sign_out::call(self, out);
}
// aten::_foreach_sign.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sign_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_sign_out::call(self, out);
}

// aten::_foreach_sin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sin_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_sin_out::call(self, out);
}
// aten::_foreach_sin.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sin_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_sin_out::call(self, out);
}

// aten::_foreach_sinh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sinh_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_sinh_out::call(self, out);
}
// aten::_foreach_sinh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sinh_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_sinh_out::call(self, out);
}

// aten::_foreach_sqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sqrt_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_sqrt_out::call(self, out);
}
// aten::_foreach_sqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_sqrt_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_sqrt_out::call(self, out);
}

// aten::_foreach_tan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_tan_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_tan_out::call(self, out);
}
// aten::_foreach_tan.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_tan_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_tan_out::call(self, out);
}

// aten::_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_tanh_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_tanh_out::call(self, out);
}
// aten::_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_tanh_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_tanh_out::call(self, out);
}

// aten::_foreach_trunc.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_trunc_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_trunc_out::call(self, out);
}
// aten::_foreach_trunc.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_trunc_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_trunc_out::call(self, out);
}

// aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_zero_out(at::TensorList out, at::TensorList self) {
    return at::_ops::_foreach_zero_out::call(self, out);
}
// aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
inline void _foreach_zero_outf(at::TensorList self, at::TensorList out) {
    return at::_ops::_foreach_zero_out::call(self, out);
}

// aten::_foreach_zero(Tensor[] self) -> Tensor[] self_out
inline ::std::vector<at::Tensor> _foreach_zero(at::TensorList self) {
    return at::_ops::_foreach_zero::call(self);
}

// aten::_foreach_copy.out(Tensor[] self, Tensor[] src, bool non_blocking=False, *, Tensor(a!)[] out) -> ()
inline void _foreach_copy_out(at::TensorList out, at::TensorList self, at::TensorList src, bool non_blocking=false) {
    return at::_ops::_foreach_copy_out::call(self, src, non_blocking, out);
}
// aten::_foreach_copy.out(Tensor[] self, Tensor[] src, bool non_blocking=False, *, Tensor(a!)[] out) -> ()
inline void _foreach_copy_outf(at::TensorList self, at::TensorList src, bool non_blocking, at::TensorList out) {
    return at::_ops::_foreach_copy_out::call(self, src, non_blocking, out);
}

// aten::bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bucketize_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & boundaries, bool out_int32=false, bool right=false) {
    return at::_ops::bucketize_Scalar_out::call(self, boundaries, out_int32, right, out);
}
// aten::bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & bucketize_outf(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) {
    return at::_ops::bucketize_Scalar_out::call(self, boundaries, out_int32, right, out);
}

// aten::glu_jvp.out(Tensor glu, Tensor x, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & glu_jvp_out(at::Tensor & out, const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim) {
    return at::_ops::glu_jvp_out::call(glu, x, dx, dim, out);
}
// aten::glu_jvp.out(Tensor glu, Tensor x, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & glu_jvp_outf(const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim, at::Tensor & out) {
    return at::_ops::glu_jvp_out::call(glu, x, dx, dim, out);
}

// aten::glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & glu_backward_jvp_out(at::Tensor & out, const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) {
    return at::_ops::glu_backward_jvp_out::call(grad_x, grad_glu, x, dgrad_glu, dx, dim, out);
}
// aten::glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & glu_backward_jvp_outf(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim, at::Tensor & out) {
    return at::_ops::glu_backward_jvp_out::call(grad_x, grad_glu, x, dgrad_glu, dx, dim, out);
}

// aten::hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardswish_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::hardswish_backward_out::call(grad_output, self, out);
}
// aten::hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & hardswish_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) {
    return at::_ops::hardswish_backward_out::call(grad_output, self, out);
}

// aten::rrelu_with_noise_backward.out(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rrelu_with_noise_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result) {
    return at::_ops::rrelu_with_noise_backward_out::call(grad_output, self, noise, lower, upper, training, self_is_result, out);
}
// aten::rrelu_with_noise_backward.out(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & rrelu_with_noise_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result, at::Tensor & out) {
    return at::_ops::rrelu_with_noise_backward_out::call(grad_output, self, noise, lower, upper, training, self_is_result, out);
}

// aten::mkldnn_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_adaptive_avg_pool2d_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::mkldnn_adaptive_avg_pool2d_backward_out::call(grad_output, self, out);
}
// aten::mkldnn_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & mkldnn_adaptive_avg_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) {
    return at::_ops::mkldnn_adaptive_avg_pool2d_backward_out::call(grad_output, self, out);
}

// aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool2d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool2d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool2d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _adaptive_avg_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool2d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool2d_out::call(self, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _adaptive_avg_pool2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool2d_out::call(self, output_size, out);
  }
}

// aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool2d_out::call(self, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _adaptive_avg_pool2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool2d_out::call(self, output_size, out);
  }
}

// aten::_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool2d_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::_adaptive_avg_pool2d_backward_out::call(grad_output, self, out);
}
// aten::_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool2d_backward_out::call(grad_output, self, out);
}

// aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool3d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _adaptive_avg_pool3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool3d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool3d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool3d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _adaptive_avg_pool3d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool3d_out::call(self, c10::fromIntArrayRefSlow(output_size), out);
  }
}

// aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool3d_out::call(self, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _adaptive_avg_pool3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) {
    return at::_ops::_adaptive_avg_pool3d_out::call(self, output_size, out);
  }
}

// aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool3d_out::call(self, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _adaptive_avg_pool3d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool3d_out::call(self, output_size, out);
  }
}

// aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool3d_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) {
    return at::_ops::_adaptive_avg_pool3d_backward_out::call(grad_output, self, out);
}
// aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _adaptive_avg_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_adaptive_avg_pool3d_backward_out::call(grad_output, self, out);
}

// aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask) {
    return at::_ops::_slow_conv2d_backward_output_mask_out::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask) {
    return at::_ops::_slow_conv2d_backward_output_mask_out::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask, out0, out1, out2);
  }
}

// aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_slow_conv2d_backward_output_mask_out::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_slow_conv2d_backward_output_mask_out::call(grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask, out0, out1, out2);
  }
}

// aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array<bool,3> output_mask) {
    return at::_ops::_slow_conv2d_backward_output_mask_out::call(grad_output, self, weight, kernel_size, stride, padding, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array<bool,3> output_mask) {
    return at::_ops::_slow_conv2d_backward_output_mask_out::call(grad_output, self, weight, kernel_size, stride, padding, output_mask, out0, out1, out2);
  }
}

// aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_slow_conv2d_backward_output_mask_out::call(grad_output, self, weight, kernel_size, stride, padding, output_mask, out0, out1, out2);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> _slow_conv2d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
    return at::_ops::_slow_conv2d_backward_output_mask_out::call(grad_output, self, weight, kernel_size, stride, padding, output_mask, out0, out1, out2);
  }
}

// aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & conv_depthwise3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) {
    return at::_ops::conv_depthwise3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & conv_depthwise3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) {
    return at::_ops::conv_depthwise3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & conv_depthwise3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::conv_depthwise3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & conv_depthwise3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::conv_depthwise3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & conv_depthwise3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) {
    return at::_ops::conv_depthwise3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & conv_depthwise3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) {
    return at::_ops::conv_depthwise3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
  }
}

// aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & conv_depthwise3d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::conv_depthwise3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & conv_depthwise3d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::conv_depthwise3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
  }
}

// aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_dilated2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_dilated2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv_dilated2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_dilated2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_dilated2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_dilated2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv_dilated2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_dilated2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_dilated2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_dilated2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv_dilated2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_dilated2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
  }
}

// aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_dilated2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_dilated2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv_dilated2d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_dilated2d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
  }
}

// aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_dilated3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_dilated3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv_dilated3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) {
    return at::_ops::slow_conv_dilated3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_dilated3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_dilated3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slow_conv_dilated3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_dilated3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out);
  }
}

// aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_dilated3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_dilated3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv_dilated3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) {
    return at::_ops::slow_conv_dilated3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
  }
}

// aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slow_conv_dilated3d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_dilated3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slow_conv_dilated3d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) {
    return at::_ops::slow_conv_dilated3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out);
  }
}

// aten::isinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isinf_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::isinf_out::call(self, out);
}
// aten::isinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & isinf_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::isinf_out::call(self, out);
}

// aten::linalg_matrix_exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_exp_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::linalg_matrix_exp_out::call(self, out);
}
// aten::linalg_matrix_exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & linalg_matrix_exp_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::linalg_matrix_exp_out::call(self, out);
}

// aten::_test_optional_intlist.out(Tensor values, int[]? addends, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_optional_intlist_out(at::Tensor & out, const at::Tensor & values, at::OptionalIntArrayRef addends) {
    return at::_ops::_test_optional_intlist_out::call(values, addends, out);
}
// aten::_test_optional_intlist.out(Tensor values, int[]? addends, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_optional_intlist_outf(const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out) {
    return at::_ops::_test_optional_intlist_out::call(values, addends, out);
}

// aten::_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_optional_filled_intlist_out(at::Tensor & out, const at::Tensor & values, at::OptionalIntArrayRef addends) {
    return at::_ops::_test_optional_filled_intlist_out::call(values, addends, out);
}
// aten::_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_optional_filled_intlist_outf(const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out) {
    return at::_ops::_test_optional_filled_intlist_out::call(values, addends, out);
}

// aten::_test_optional_floatlist.out(Tensor values, float[]? addends, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_optional_floatlist_out(at::Tensor & out, const at::Tensor & values, ::std::optional<at::ArrayRef<double>> addends) {
    return at::_ops::_test_optional_floatlist_out::call(values, addends, out);
}
// aten::_test_optional_floatlist.out(Tensor values, float[]? addends, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_optional_floatlist_outf(const at::Tensor & values, ::std::optional<at::ArrayRef<double>> addends, at::Tensor & out) {
    return at::_ops::_test_optional_floatlist_out::call(values, addends, out);
}

// aten::_test_warn_in_autograd.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_warn_in_autograd_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_test_warn_in_autograd_out::call(self, out);
}
// aten::_test_warn_in_autograd.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_warn_in_autograd_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_test_warn_in_autograd_out::call(self, out);
}

// aten::_test_autograd_multiple_dispatch.fullcoverage_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_autograd_multiple_dispatch_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_test_autograd_multiple_dispatch_fullcoverage_out::call(self, out);
}
// aten::_test_autograd_multiple_dispatch.fullcoverage_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_autograd_multiple_dispatch_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_test_autograd_multiple_dispatch_fullcoverage_out::call(self, out);
}

// aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_autograd_multiple_dispatch_view_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_test_autograd_multiple_dispatch_view_copy_out::call(self, out);
}
// aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _test_autograd_multiple_dispatch_view_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_test_autograd_multiple_dispatch_view_copy_out::call(self, out);
}

// aten::segment_reduce.out(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & segment_reduce_out(at::Tensor & out, const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths={}, const ::std::optional<at::Tensor> & indices={}, const ::std::optional<at::Tensor> & offsets={}, int64_t axis=0, bool unsafe=false, const ::std::optional<at::Scalar> & initial=::std::nullopt) {
    return at::_ops::segment_reduce_out::call(data, reduce, lengths, indices, offsets, axis, unsafe, initial, out);
}
// aten::segment_reduce.out(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & segment_reduce_outf(const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths, const ::std::optional<at::Tensor> & indices, const ::std::optional<at::Tensor> & offsets, int64_t axis, bool unsafe, const ::std::optional<at::Scalar> & initial, at::Tensor & out) {
    return at::_ops::segment_reduce_out::call(data, reduce, lengths, indices, offsets, axis, unsafe, initial, out);
}

// aten::_segment_reduce_backward.out(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _segment_reduce_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths={}, const ::std::optional<at::Tensor> & offsets={}, int64_t axis=0, const ::std::optional<at::Scalar> & initial=::std::nullopt) {
    return at::_ops::_segment_reduce_backward_out::call(grad, output, data, reduce, lengths, offsets, axis, initial, out);
}
// aten::_segment_reduce_backward.out(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _segment_reduce_backward_outf(const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const ::std::optional<at::Tensor> & lengths, const ::std::optional<at::Tensor> & offsets, int64_t axis, const ::std::optional<at::Scalar> & initial, at::Tensor & out) {
    return at::_ops::_segment_reduce_backward_out::call(grad, output, data, reduce, lengths, offsets, axis, initial, out);
}

// aten::_nested_tensor_from_tensor_list.out(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_from_tensor_list_out(at::Tensor & out, at::TensorList list, ::std::optional<at::ScalarType> dtype=::std::nullopt, ::std::optional<at::Layout> layout=::std::nullopt, ::std::optional<at::Device> device=::std::nullopt, ::std::optional<bool> pin_memory=::std::nullopt) {
    return at::_ops::_nested_tensor_from_tensor_list_out::call(list, dtype, layout, device, pin_memory, out);
}
// aten::_nested_tensor_from_tensor_list.out(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _nested_tensor_from_tensor_list_outf(at::TensorList list, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, at::Tensor & out) {
    return at::_ops::_nested_tensor_from_tensor_list_out::call(list, dtype, layout, device, pin_memory, out);
}

// aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fw_primal_copy_out(at::Tensor & out, const at::Tensor & self, int64_t level) {
    return at::_ops::_fw_primal_copy_out::call(self, level, out);
}
// aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _fw_primal_copy_outf(const at::Tensor & self, int64_t level, at::Tensor & out) {
    return at::_ops::_fw_primal_copy_out::call(self, level, out);
}

// aten::_make_dual_copy.out(Tensor primal, Tensor tangent, int level, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _make_dual_copy_out(at::Tensor & out, const at::Tensor & primal, const at::Tensor & tangent, int64_t level) {
    return at::_ops::_make_dual_copy_out::call(primal, tangent, level, out);
}
// aten::_make_dual_copy.out(Tensor primal, Tensor tangent, int level, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _make_dual_copy_outf(const at::Tensor & primal, const at::Tensor & tangent, int64_t level, at::Tensor & out) {
    return at::_ops::_make_dual_copy_out::call(primal, tangent, level, out);
}

// aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_as_real_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::view_as_real_copy_out::call(self, out);
}
// aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_as_real_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::view_as_real_copy_out::call(self, out);
}

// aten::view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_as_complex_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::view_as_complex_copy_out::call(self, out);
}
// aten::view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_as_complex_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::view_as_complex_copy_out::call(self, out);
}

// aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _conj_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_conj_copy_out::call(self, out);
}
// aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _conj_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_conj_copy_out::call(self, out);
}

// aten::_neg_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _neg_view_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_neg_view_copy_out::call(self, out);
}
// aten::_neg_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _neg_view_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_neg_view_copy_out::call(self, out);
}

// aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & as_strided_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_copy_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & as_strided_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_copy_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt, out);
  }
}

// aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & as_strided_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset, at::Tensor & out) {
    return at::_ops::as_strided_copy_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & as_strided_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, ::std::optional<int64_t> storage_offset, at::Tensor & out) {
    return at::_ops::as_strided_copy_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? ::std::make_optional(c10::SymInt(*storage_offset)) : ::std::nullopt, out);
  }
}

// aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & as_strided_copy_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_copy_out::call(self, size, stride, storage_offset, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & as_strided_copy_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset=::std::nullopt) {
    return at::_ops::as_strided_copy_out::call(self, size, stride, storage_offset, out);
  }
}

// aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & as_strided_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset, at::Tensor & out) {
    return at::_ops::as_strided_copy_out::call(self, size, stride, storage_offset, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & as_strided_copy_outf(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset, at::Tensor & out) {
    return at::_ops::as_strided_copy_out::call(self, size, stride, storage_offset, out);
  }
}

// aten::_sparse_broadcast_to_copy.out(Tensor self, int[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_broadcast_to_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::_sparse_broadcast_to_copy_out::call(self, size, out);
}
// aten::_sparse_broadcast_to_copy.out(Tensor self, int[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _sparse_broadcast_to_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::_sparse_broadcast_to_copy_out::call(self, size, out);
}

// aten::diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diagonal_copy_out(at::Tensor & out, const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) {
    return at::_ops::diagonal_copy_out::call(self, offset, dim1, dim2, out);
}
// aten::diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & diagonal_copy_outf(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) {
    return at::_ops::diagonal_copy_out::call(self, offset, dim1, dim2, out);
}

// aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & expand_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, bool implicit=false) {
    return at::_ops::expand_copy_out::call(self, c10::fromIntArrayRefSlow(size), implicit, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & expand_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, bool implicit=false) {
    return at::_ops::expand_copy_out::call(self, c10::fromIntArrayRefSlow(size), implicit, out);
  }
}

// aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & expand_copy_outf(const at::Tensor & self, at::IntArrayRef size, bool implicit, at::Tensor & out) {
    return at::_ops::expand_copy_out::call(self, c10::fromIntArrayRefSlow(size), implicit, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & expand_copy_outf(const at::Tensor & self, at::IntArrayRef size, bool implicit, at::Tensor & out) {
    return at::_ops::expand_copy_out::call(self, c10::fromIntArrayRefSlow(size), implicit, out);
  }
}

// aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & expand_copy_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit=false) {
    return at::_ops::expand_copy_out::call(self, size, implicit, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & expand_copy_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit=false) {
    return at::_ops::expand_copy_out::call(self, size, implicit, out);
  }
}

// aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & expand_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit, at::Tensor & out) {
    return at::_ops::expand_copy_out::call(self, size, implicit, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & expand_copy_outf(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit, at::Tensor & out) {
    return at::_ops::expand_copy_out::call(self, size, implicit, out);
  }
}

// aten::permute_copy.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & permute_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dims) {
    return at::_ops::permute_copy_out::call(self, dims, out);
}
// aten::permute_copy.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & permute_copy_outf(const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out) {
    return at::_ops::permute_copy_out::call(self, dims, out);
}

// aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _reshape_alias_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::_reshape_alias_copy_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _reshape_alias_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) {
    return at::_ops::_reshape_alias_copy_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _reshape_alias_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::_reshape_alias_copy_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & _reshape_alias_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
    return at::_ops::_reshape_alias_copy_out::call(self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
  }
}

// aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _reshape_alias_copy_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::_reshape_alias_copy_out::call(self, size, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _reshape_alias_copy_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) {
    return at::_ops::_reshape_alias_copy_out::call(self, size, stride, out);
  }
}

// aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _reshape_alias_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::_reshape_alias_copy_out::call(self, size, stride, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & _reshape_alias_copy_outf(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
    return at::_ops::_reshape_alias_copy_out::call(self, size, stride, out);
  }
}

// aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, int64_t index) {
    return at::_ops::select_copy_int_out::call(self, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & select_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, int64_t index) {
    return at::_ops::select_copy_int_out::call(self, dim, index, out);
  }
}

// aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_copy_outf(const at::Tensor & self, int64_t dim, int64_t index, at::Tensor & out) {
    return at::_ops::select_copy_int_out::call(self, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & select_copy_outf(const at::Tensor & self, int64_t dim, int64_t index, at::Tensor & out) {
    return at::_ops::select_copy_int_out::call(self, dim, index, out);
  }
}

// aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_copy_symint_out(at::Tensor & out, const at::Tensor & self, int64_t dim, c10::SymInt index) {
    return at::_ops::select_copy_int_out::call(self, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & select_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, c10::SymInt index) {
    return at::_ops::select_copy_int_out::call(self, dim, index, out);
  }
}

// aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & select_copy_symint_outf(const at::Tensor & self, int64_t dim, c10::SymInt index, at::Tensor & out) {
    return at::_ops::select_copy_int_out::call(self, dim, index, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & select_copy_outf(const at::Tensor & self, int64_t dim, c10::SymInt index, at::Tensor & out) {
    return at::_ops::select_copy_int_out::call(self, dim, index, out);
  }
}

// aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & detach_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::detach_copy_out::call(self, out);
}
// aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & detach_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::detach_copy_out::call(self, out);
}

// aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_copy_Tensor_out::call(self, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slice_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim=0, ::std::optional<int64_t> start=::std::nullopt, ::std::optional<int64_t> end=::std::nullopt, int64_t step=1) {
    return at::_ops::slice_copy_Tensor_out::call(self, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step, out);
  }
}

// aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_copy_outf(const at::Tensor & self, int64_t dim, ::std::optional<int64_t> start, ::std::optional<int64_t> end, int64_t step, at::Tensor & out) {
    return at::_ops::slice_copy_Tensor_out::call(self, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & slice_copy_outf(const at::Tensor & self, int64_t dim, ::std::optional<int64_t> start, ::std::optional<int64_t> end, int64_t step, at::Tensor & out) {
    return at::_ops::slice_copy_Tensor_out::call(self, dim, start.has_value() ? ::std::make_optional(c10::SymInt(*start)) : ::std::nullopt, end.has_value() ? ::std::make_optional(c10::SymInt(*end)) : ::std::nullopt, step, out);
  }
}

// aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_copy_symint_out(at::Tensor & out, const at::Tensor & self, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_copy_Tensor_out::call(self, dim, start, end, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slice_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim=0, ::std::optional<c10::SymInt> start=::std::nullopt, ::std::optional<c10::SymInt> end=::std::nullopt, c10::SymInt step=1) {
    return at::_ops::slice_copy_Tensor_out::call(self, dim, start, end, step, out);
  }
}

// aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & slice_copy_symint_outf(const at::Tensor & self, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) {
    return at::_ops::slice_copy_Tensor_out::call(self, dim, start, end, step, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & slice_copy_outf(const at::Tensor & self, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step, at::Tensor & out) {
    return at::_ops::slice_copy_Tensor_out::call(self, dim, start, end, step, out);
  }
}

// aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::squeeze_copy_out::call(self, out);
}
// aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & squeeze_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::squeeze_copy_out::call(self, out);
}

// aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim) {
    return at::_ops::squeeze_copy_dim_out::call(self, dim, out);
}
// aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & squeeze_copy_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) {
    return at::_ops::squeeze_copy_dim_out::call(self, dim, out);
}

// aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim) {
    return at::_ops::squeeze_copy_dims_out::call(self, dim, out);
}
// aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & squeeze_copy_outf(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) {
    return at::_ops::squeeze_copy_dims_out::call(self, dim, out);
}

// aten::t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & t_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::t_copy_out::call(self, out);
}
// aten::t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & t_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::t_copy_out::call(self, out);
}

// aten::transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & transpose_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim0, int64_t dim1) {
    return at::_ops::transpose_copy_int_out::call(self, dim0, dim1, out);
}
// aten::transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & transpose_copy_outf(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) {
    return at::_ops::transpose_copy_int_out::call(self, dim0, dim1, out);
}

// aten::unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & unsqueeze_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim) {
    return at::_ops::unsqueeze_copy_out::call(self, dim, out);
}
// aten::unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & unsqueeze_copy_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) {
    return at::_ops::unsqueeze_copy_out::call(self, dim, out);
}

// aten::_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _indices_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_indices_copy_out::call(self, out);
}
// aten::_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _indices_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_indices_copy_out::call(self, out);
}

// aten::_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _values_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::_values_copy_out::call(self, out);
}
// aten::_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _values_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::_values_copy_out::call(self, out);
}

// aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & indices_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::indices_copy_out::call(self, out);
}
// aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & indices_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::indices_copy_out::call(self, out);
}

// aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & values_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::values_copy_out::call(self, out);
}
// aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & values_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::values_copy_out::call(self, out);
}

// aten::crow_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & crow_indices_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::crow_indices_copy_out::call(self, out);
}
// aten::crow_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & crow_indices_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::crow_indices_copy_out::call(self, out);
}

// aten::col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & col_indices_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::col_indices_copy_out::call(self, out);
}
// aten::col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & col_indices_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::col_indices_copy_out::call(self, out);
}

// aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ccol_indices_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::ccol_indices_copy_out::call(self, out);
}
// aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & ccol_indices_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::ccol_indices_copy_out::call(self, out);
}

// aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & row_indices_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::row_indices_copy_out::call(self, out);
}
// aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & row_indices_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::row_indices_copy_out::call(self, out);
}

// aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::view_copy_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
    return at::_ops::view_copy_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::view_copy_out::call(self, c10::fromIntArrayRefSlow(size), out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & view_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
    return at::_ops::view_copy_out::call(self, c10::fromIntArrayRefSlow(size), out);
  }
}

// aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_copy_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::view_copy_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
    return at::_ops::view_copy_out::call(self, size, out);
  }
}

// aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::view_copy_out::call(self, size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & view_copy_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
    return at::_ops::view_copy_out::call(self, size, out);
  }
}

// aten::view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, at::ScalarType dtype) {
    return at::_ops::view_copy_dtype_out::call(self, dtype, out);
}
// aten::view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & view_copy_outf(const at::Tensor & self, at::ScalarType dtype, at::Tensor & out) {
    return at::_ops::view_copy_dtype_out::call(self, dtype, out);
}

// aten::unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & unfold_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) {
    return at::_ops::unfold_copy_out::call(self, dimension, size, step, out);
}
// aten::unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & unfold_copy_outf(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step, at::Tensor & out) {
    return at::_ops::unfold_copy_out::call(self, dimension, size, step, out);
}

// aten::alias_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & alias_copy_out(at::Tensor & out, const at::Tensor & self) {
    return at::_ops::alias_copy_out::call(self, out);
}
// aten::alias_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & alias_copy_outf(const at::Tensor & self, at::Tensor & out) {
    return at::_ops::alias_copy_out::call(self, out);
}

// aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & to_padded_tensor_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt) {
    return at::_ops::to_padded_tensor_out::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & to_padded_tensor_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt) {
    return at::_ops::to_padded_tensor_out::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, out);
  }
}

// aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & to_padded_tensor_outf(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::to_padded_tensor_out::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
  at::Tensor & to_padded_tensor_outf(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::to_padded_tensor_out::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, out);
  }
}

// aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & to_padded_tensor_symint_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=::std::nullopt) {
    return at::_ops::to_padded_tensor_out::call(self, padding, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & to_padded_tensor_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=::std::nullopt) {
    return at::_ops::to_padded_tensor_out::call(self, padding, output_size, out);
  }
}

// aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & to_padded_tensor_symint_outf(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::to_padded_tensor_out::call(self, padding, output_size, out);
}
namespace symint {
  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
  at::Tensor & to_padded_tensor_outf(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out) {
    return at::_ops::to_padded_tensor_out::call(self, padding, output_size, out);
  }
}

// aten::_transformer_encoder_layer_fwd.out(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _transformer_encoder_layer_fwd_out(at::Tensor & out, const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const ::std::optional<at::Tensor> & mask={}, ::std::optional<int64_t> mask_type=::std::nullopt) {
    return at::_ops::_transformer_encoder_layer_fwd_out::call(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type, out);
}
// aten::_transformer_encoder_layer_fwd.out(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _transformer_encoder_layer_fwd_outf(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const ::std::optional<at::Tensor> & mask, ::std::optional<int64_t> mask_type, at::Tensor & out) {
    return at::_ops::_transformer_encoder_layer_fwd_out::call(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type, out);
}

// aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _native_multi_head_attention_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const ::std::optional<at::Tensor> & mask={}, bool need_weights=true, bool average_attn_weights=true, ::std::optional<int64_t> mask_type=::std::nullopt) {
    return at::_ops::_native_multi_head_attention_out::call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type, out0, out1);
}
// aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
inline ::std::tuple<at::Tensor &,at::Tensor &> _native_multi_head_attention_outf(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const ::std::optional<at::Tensor> & mask, bool need_weights, bool average_attn_weights, ::std::optional<int64_t> mask_type, at::Tensor & out0, at::Tensor & out1) {
    return at::_ops::_native_multi_head_attention_out::call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type, out0, out1);
}

// aten::_triton_scaled_dot_attention.out(Tensor q, Tensor k, Tensor v, float dropout_p=0.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _triton_scaled_dot_attention_out(at::Tensor & out, const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p=0.0) {
    return at::_ops::_triton_scaled_dot_attention_out::call(q, k, v, dropout_p, out);
}
// aten::_triton_scaled_dot_attention.out(Tensor q, Tensor k, Tensor v, float dropout_p=0.0, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _triton_scaled_dot_attention_outf(const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p, at::Tensor & out) {
    return at::_ops::_triton_scaled_dot_attention_out::call(q, k, v, dropout_p, out);
}

// aten::_triton_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _triton_multi_head_attention_out(at::Tensor & out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const ::std::optional<at::Tensor> & mask={}) {
    return at::_ops::_triton_multi_head_attention_out::call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, out);
}
// aten::_triton_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, *, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _triton_multi_head_attention_outf(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const ::std::optional<at::Tensor> & mask, at::Tensor & out) {
    return at::_ops::_triton_multi_head_attention_out::call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, out);
}

// aten::_foobar.out(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _foobar_out(at::Tensor & out, const at::Tensor & self, bool arg1=true, bool arg2=true, bool arg3=true) {
    return at::_ops::_foobar_out::call(self, arg1, arg2, arg3, out);
}
// aten::_foobar.out(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True, Tensor(a!) out) -> Tensor(a!)
inline at::Tensor & _foobar_outf(const at::Tensor & self, bool arg1, bool arg2, bool arg3, at::Tensor & out) {
    return at::_ops::_foobar_out::call(self, arg1, arg2, arg3, out);
}

// aten::_fused_adam.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adam_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adam_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out);
}
// aten::_fused_adam.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adam_outf(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out) {
    return at::_ops::_fused_adam_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out);
}

// aten::_fused_adam(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)
inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adam(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adam::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}

// aten::_fused_adam.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adam_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adam_tensor_lr_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out);
}
// aten::_fused_adam.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adam_outf(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out) {
    return at::_ops::_fused_adam_tensor_lr_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out);
}

// aten::_fused_adam.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)
inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adam(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adam_tensor_lr::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}

// aten::_fused_adamw.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adamw_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adamw_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out);
}
// aten::_fused_adamw.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adamw_outf(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out) {
    return at::_ops::_fused_adamw_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out);
}

// aten::_fused_adamw(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)
inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adamw(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adamw::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}

// aten::_fused_adamw.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adamw_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adamw_tensor_lr_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out);
}
// aten::_fused_adamw.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adamw_outf(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out) {
    return at::_ops::_fused_adamw_tensor_lr_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out);
}

// aten::_fused_adamw.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out)
inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adamw(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adamw_tensor_lr::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}

// aten::_fused_sgd.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_sgd_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_sgd_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
}
// aten::_fused_sgd.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_sgd_outf(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out) {
    return at::_ops::_fused_sgd_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
}

// aten::_fused_sgd(Tensor[] self, Tensor[] grads, Tensor[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out)
inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_sgd(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_sgd::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
}

// aten::_fused_sgd.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_sgd_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_sgd_tensor_lr_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
}
// aten::_fused_sgd.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_sgd_outf(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out) {
    return at::_ops::_fused_sgd_tensor_lr_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out);
}

// aten::_fused_sgd.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out)
inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_sgd(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_sgd_tensor_lr::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf);
}

// aten::_fused_adagrad.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] state_sums, Tensor(d!)[] state_steps, *, float lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adagrad_out(at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList state_sums, at::TensorList state_steps, double lr, double lr_decay, double weight_decay, double eps, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adagrad_out::call(self, grads, state_sums, state_steps, lr, lr_decay, weight_decay, eps, maximize, grad_scale, found_inf, out);
}
// aten::_fused_adagrad.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] state_sums, Tensor(d!)[] state_steps, *, float lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> ()
inline void _fused_adagrad_outf(at::TensorList self, at::TensorList grads, at::TensorList state_sums, at::TensorList state_steps, double lr, double lr_decay, double weight_decay, double eps, bool maximize, const ::std::optional<at::Tensor> & grad_scale, const ::std::optional<at::Tensor> & found_inf, at::TensorList out) {
    return at::_ops::_fused_adagrad_out::call(self, grads, state_sums, state_steps, lr, lr_decay, weight_decay, eps, maximize, grad_scale, found_inf, out);
}

// aten::_fused_adagrad(Tensor[] self, Tensor[] grads, Tensor[] state_sums, Tensor[] state_steps, *, float lr, float lr_decay, float weight_decay, float eps, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] state_sums_out, Tensor[] state_steps_out)
inline ::std::tuple<::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>,::std::vector<at::Tensor>> _fused_adagrad(at::TensorList self, at::TensorList grads, at::TensorList state_sums, at::TensorList state_steps, double lr, double lr_decay, double weight_decay, double eps, bool maximize, const ::std::optional<at::Tensor> & grad_scale={}, const ::std::optional<at::Tensor> & found_inf={}) {
    return at::_ops::_fused_adagrad::call(self, grads, state_sums, state_steps, lr, lr_decay, weight_decay, eps, maximize, grad_scale, found_inf);
}

// Special C++ only overloads for std()-like functions (See gh-40287)
// These are needed because int -> bool conversion takes precedence over int -> IntArrayRef
// So, for example std(0) would select the std(unbiased=False) overload
TORCH_API inline Tensor var(const Tensor& self, int dim) {
  return at::var(self, IntArrayRef{dim});
}
TORCH_API inline std::tuple<Tensor, Tensor> var_mean(const Tensor& self, int dim) {
  return at::var_mean(self, IntArrayRef{dim});
}
TORCH_API inline Tensor std(const Tensor& self, int dim) {
  return at::std(self, IntArrayRef{dim});
}
TORCH_API inline std::tuple<Tensor, Tensor> std_mean(const Tensor& self, int dim) {
  return at::std_mean(self, IntArrayRef{dim});
}

inline int64_t numel(const Tensor& tensor) {
  return tensor.numel();
}

inline int64_t size(const Tensor& tensor, int64_t dim) {
  return tensor.size(dim);
}

inline int64_t stride(const Tensor& tensor, int64_t dim) {
  return tensor.stride(dim);
}

inline bool is_complex(const Tensor& tensor) {
  return tensor.is_complex();
}

inline bool is_floating_point(const Tensor& tensor) {
  return tensor.is_floating_point();
}

inline bool is_signed(const Tensor& tensor) {
  return tensor.is_signed();
}

inline bool is_inference(const Tensor& tensor) {
  return tensor.is_inference();
}

inline bool _is_zerotensor(const Tensor& tensor) {
  return tensor._is_zerotensor();
}

inline bool is_conj(const Tensor& tensor) {
  return tensor.is_conj();
}

inline Tensor conj(const Tensor& tensor) {
  return tensor.conj();
}

inline bool is_neg(const Tensor& tensor) {
  return tensor.is_neg();
}

}