TorchScript Builtins¶
This is a full reference of functions and Tensor methods accessible in TorchScript
Supported Tensor Methods¶
Tensor.__and__(other : number) -> Tensor
Tensor.__and__(other : Tensor) -> Tensor
Tensor.__iand__(other : number) -> Tensor
Tensor.__iand__(other : Tensor) -> Tensor
Tensor.__ilshift__(other : number) -> Tensor
Tensor.__ilshift__(other : Tensor) -> Tensor
Tensor.__ior__(other : number) -> Tensor
Tensor.__ior__(other : Tensor) -> Tensor
Tensor.__irshift__(other : number) -> Tensor
Tensor.__irshift__(other : Tensor) -> Tensor
Tensor.__ixor__(other : number) -> Tensor
Tensor.__ixor__(other : Tensor) -> Tensor
Tensor.__lshift__(other : number) -> Tensor
Tensor.__lshift__(other : Tensor) -> Tensor
Tensor.__or__(other : number) -> Tensor
Tensor.__or__(other : Tensor) -> Tensor
Tensor.__rshift__(other : number) -> Tensor
Tensor.__rshift__(other : Tensor) -> Tensor
Tensor.__xor__(other : number) -> Tensor
Tensor.__xor__(other : Tensor) -> Tensor
Tensor.abs() -> Tensor
Tensor.abs(out : Tensor) -> Tensor
Tensor.abs_() -> Tensor
Tensor.absolute() -> Tensor
Tensor.absolute(out : Tensor) -> Tensor
Tensor.absolute_() -> Tensor
Tensor.acos() -> Tensor
Tensor.acos(out : Tensor) -> Tensor
Tensor.acos_() -> Tensor
Tensor.acosh() -> Tensor
Tensor.acosh(out : Tensor) -> Tensor
Tensor.acosh_() -> Tensor
Tensor.add(other : Tensor,
alpha : number=1) -> Tensor
Tensor.add(other : number,
alpha : number=1) -> Tensor
Tensor.add(other : Tensor,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.add_(other : number,
alpha : number=1) -> Tensor
Tensor.add_(other : Tensor,
alpha : number=1) -> Tensor
Tensor.addbmm(batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.addbmm(batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.addbmm_(batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.addcdiv(tensor1 : Tensor,
tensor2 : Tensor,
value : number=1) -> Tensor
Tensor.addcdiv(tensor1 : Tensor,
tensor2 : Tensor,
value : number=1,
out : Tensor) -> Tensor
Tensor.addcdiv_(tensor1 : Tensor,
tensor2 : Tensor,
value : number=1) -> Tensor
Tensor.addcmul(tensor1 : Tensor,
tensor2 : Tensor,
value : number=1) -> Tensor
Tensor.addcmul(tensor1 : Tensor,
tensor2 : Tensor,
value : number=1,
out : Tensor) -> Tensor
Tensor.addcmul_(tensor1 : Tensor,
tensor2 : Tensor,
value : number=1) -> Tensor
Tensor.addmm(mat1 : Tensor,
mat2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.addmm(mat1 : Tensor,
mat2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.addmm_(mat1 : Tensor,
mat2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.addmv(mat : Tensor,
vec : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.addmv(mat : Tensor,
vec : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.addmv_(mat : Tensor,
vec : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.addr(vec1 : Tensor,
vec2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.addr(vec1 : Tensor,
vec2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.addr_(vec1 : Tensor,
vec2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.align_as(other : Tensor) -> Tensor
Tensor.align_to(names : List[str]) -> Tensor
Tensor.align_to(order : List[str],
ellipsis_idx : int) -> Tensor
Tensor.all() -> Tensor
Tensor.all(dim : int,
keepdim : bool=False) -> Tensor
Tensor.all(dim : int,
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.all(out : Tensor) -> Tensor
Tensor.all(dim : str,
keepdim : bool=False) -> Tensor
Tensor.all(dim : str,
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.allclose(other : Tensor,
rtol : float=1e-05,
atol : float=1e-08,
equal_nan : bool=False) -> bool
Tensor.amax(dim : List[int]=[],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.amax(dim : List[int]=[],
keepdim : bool=False) -> Tensor
Tensor.amin(dim : List[int]=[],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.amin(dim : List[int]=[],
keepdim : bool=False) -> Tensor
Tensor.aminmax(dim : Optional[int],
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.aminmax(dim : Optional[int],
keepdim : bool=False,
min : Tensor,
max : Tensor) -> Tuple[Tensor, Tensor]
Tensor.angle() -> Tensor
Tensor.angle(out : Tensor) -> Tensor
Tensor.any() -> Tensor
Tensor.any(dim : int,
keepdim : bool=False) -> Tensor
Tensor.any(dim : int,
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.any(out : Tensor) -> Tensor
Tensor.any(dim : str,
keepdim : bool=False) -> Tensor
Tensor.any(dim : str,
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.arccos() -> Tensor
Tensor.arccos(out : Tensor) -> Tensor
Tensor.arccos_() -> Tensor
Tensor.arccosh() -> Tensor
Tensor.arccosh(out : Tensor) -> Tensor
Tensor.arccosh_() -> Tensor
Tensor.arcsin() -> Tensor
Tensor.arcsin(out : Tensor) -> Tensor
Tensor.arcsin_() -> Tensor
Tensor.arcsinh() -> Tensor
Tensor.arcsinh(out : Tensor) -> Tensor
Tensor.arcsinh_() -> Tensor
Tensor.arctan() -> Tensor
Tensor.arctan(out : Tensor) -> Tensor
Tensor.arctan_() -> Tensor
Tensor.arctanh() -> Tensor
Tensor.arctanh(out : Tensor) -> Tensor
Tensor.arctanh_() -> Tensor
Tensor.argmax(dim : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.argmax(dim : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.argmin(dim : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.argmin(dim : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.argsort(dim : int=-1,
descending : bool=False) -> Tensor
Tensor.argsort(dim : str,
descending : bool=False) -> Tensor
Tensor.as_strided(size : List[int],
stride : List[int],
storage_offset : Optional[int]) -> Tensor
Tensor.as_strided_(size : List[int],
stride : List[int],
storage_offset : Optional[int]) -> Tensor
Tensor.asin() -> Tensor
Tensor.asin(out : Tensor) -> Tensor
Tensor.asin_() -> Tensor
Tensor.asinh() -> Tensor
Tensor.asinh(out : Tensor) -> Tensor
Tensor.asinh_() -> Tensor
Tensor.atan() -> Tensor
Tensor.atan(out : Tensor) -> Tensor
Tensor.atan2(other : Tensor) -> Tensor
Tensor.atan2(other : Tensor,
out : Tensor) -> Tensor
Tensor.atan2_(other : Tensor) -> Tensor
Tensor.atan_() -> Tensor
Tensor.atanh() -> Tensor
Tensor.atanh(out : Tensor) -> Tensor
Tensor.atanh_() -> Tensor
Tensor.backward(gradient : Optional[Tensor],
retain_graph : Optional[bool],
create_graph : bool=False) -> Tuple[]
Tensor.baddbmm(batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.baddbmm(batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.baddbmm_(batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.bernoulli(generator : Optional[Generator]) -> Tensor
Tensor.bernoulli(generator : Optional[Generator],
out : Tensor) -> Tensor
Tensor.bernoulli(p : float,
generator : Optional[Generator]) -> Tensor
Tensor.bernoulli_(p : Tensor,
generator : Optional[Generator]) -> Tensor
Tensor.bernoulli_(p : float=0.5,
generator : Optional[Generator]) -> Tensor
Tensor.bincount(weights : Optional[Tensor],
minlength : int=0) -> Tensor
Tensor.bitwise_and(other : Tensor) -> Tensor
Tensor.bitwise_and(other : Tensor,
out : Tensor) -> Tensor
Tensor.bitwise_and(other : number) -> Tensor
Tensor.bitwise_and(other : number,
out : Tensor) -> Tensor
Tensor.bitwise_and_(other : Tensor) -> Tensor
Tensor.bitwise_and_(other : number) -> Tensor
Tensor.bitwise_left_shift(other : Tensor) -> Tensor
Tensor.bitwise_left_shift(other : Tensor,
out : Tensor) -> Tensor
Tensor.bitwise_left_shift(other : number) -> Tensor
Tensor.bitwise_left_shift(other : number,
out : Tensor) -> Tensor
Tensor.bitwise_left_shift_(other : Tensor) -> Tensor
Tensor.bitwise_left_shift_(other : number) -> Tensor
Tensor.bitwise_not() -> Tensor
Tensor.bitwise_not(out : Tensor) -> Tensor
Tensor.bitwise_not_() -> Tensor
Tensor.bitwise_or(other : Tensor) -> Tensor
Tensor.bitwise_or(other : Tensor,
out : Tensor) -> Tensor
Tensor.bitwise_or(other : number,
out : Tensor) -> Tensor
Tensor.bitwise_or(other : number) -> Tensor
Tensor.bitwise_or_(other : Tensor) -> Tensor
Tensor.bitwise_or_(other : number) -> Tensor
Tensor.bitwise_right_shift(other : Tensor) -> Tensor
Tensor.bitwise_right_shift(other : Tensor,
out : Tensor) -> Tensor
Tensor.bitwise_right_shift(other : number) -> Tensor
Tensor.bitwise_right_shift(other : number,
out : Tensor) -> Tensor
Tensor.bitwise_right_shift_(other : Tensor) -> Tensor
Tensor.bitwise_right_shift_(other : number) -> Tensor
Tensor.bitwise_xor(other : Tensor) -> Tensor
Tensor.bitwise_xor(other : Tensor,
out : Tensor) -> Tensor
Tensor.bitwise_xor(other : number,
out : Tensor) -> Tensor
Tensor.bitwise_xor(other : number) -> Tensor
Tensor.bitwise_xor_(other : Tensor) -> Tensor
Tensor.bitwise_xor_(other : number) -> Tensor
Tensor.bmm(mat2 : Tensor) -> Tensor
Tensor.bmm(mat2 : Tensor,
out : Tensor) -> Tensor
Tensor.broadcast_to(size : List[int]) -> Tensor
Tensor.cauchy_(median : float=0.0,
sigma : float=1.0,
generator : Optional[Generator]) -> Tensor
Tensor.ceil() -> Tensor
Tensor.ceil(out : Tensor) -> Tensor
Tensor.ceil_() -> Tensor
Tensor.cholesky(upper : bool=False) -> Tensor
Tensor.cholesky(upper : bool=False,
out : Tensor) -> Tensor
Tensor.cholesky_inverse(upper : bool=False) -> Tensor
Tensor.cholesky_inverse(upper : bool=False,
out : Tensor) -> Tensor
Tensor.cholesky_solve(input2 : Tensor,
upper : bool=False) -> Tensor
Tensor.cholesky_solve(input2 : Tensor,
upper : bool=False,
out : Tensor) -> Tensor
Tensor.chunk(chunks : int,
dim : int=0) -> List[Tensor]
Tensor.clamp(min : Optional[number],
max : Optional[number]) -> Tensor
Tensor.clamp(min : Optional[Tensor],
max : Optional[Tensor]) -> Tensor
Tensor.clamp(min : Optional[number],
max : Optional[number],
out : Tensor) -> Tensor
Tensor.clamp(min : Optional[Tensor],
max : Optional[Tensor],
out : Tensor) -> Tensor
Tensor.clamp_(min : Optional[number],
max : Optional[number]) -> Tensor
Tensor.clamp_(min : Optional[Tensor],
max : Optional[Tensor]) -> Tensor
Tensor.clamp_max(max : number) -> Tensor
Tensor.clamp_max(max : Tensor) -> Tensor
Tensor.clamp_max(max : number,
out : Tensor) -> Tensor
Tensor.clamp_max(max : Tensor,
out : Tensor) -> Tensor
Tensor.clamp_max_(max : number) -> Tensor
Tensor.clamp_max_(max : Tensor) -> Tensor
Tensor.clamp_min(min : number) -> Tensor
Tensor.clamp_min(min : Tensor) -> Tensor
Tensor.clamp_min(min : number,
out : Tensor) -> Tensor
Tensor.clamp_min(min : Tensor,
out : Tensor) -> Tensor
Tensor.clamp_min_(min : number) -> Tensor
Tensor.clamp_min_(min : Tensor) -> Tensor
Tensor.clip(min : Optional[number],
max : Optional[number]) -> Tensor
Tensor.clip(min : Optional[number],
max : Optional[number],
out : Tensor) -> Tensor
Tensor.clip(min : Optional[Tensor],
max : Optional[Tensor]) -> Tensor
Tensor.clip(min : Optional[Tensor],
max : Optional[Tensor],
out : Tensor) -> Tensor
Tensor.clip_(min : Optional[number],
max : Optional[number]) -> Tensor
Tensor.clip_(min : Optional[Tensor],
max : Optional[Tensor]) -> Tensor
Tensor.clone(memory_format : Optional[int]) -> Tensor
Tensor.coalesce() -> Tensor
Tensor.col_indices() -> Tensor
Tensor.conj() -> Tensor
Tensor.conj_physical() -> Tensor
Tensor.conj_physical(out : Tensor) -> Tensor
Tensor.conj_physical_() -> Tensor
Tensor.contiguous(memory_format : int=0) -> Tensor
Tensor.copy_(src : Tensor,
non_blocking : bool=False) -> Tensor
Tensor.copy_(other : Tensor) -> Tensor
Tensor.copy_(other : int) -> Tensor
Tensor.copy_(other : float) -> Tensor
Tensor.copysign(other : Tensor) -> Tensor
Tensor.copysign(other : Tensor,
out : Tensor) -> Tensor
Tensor.copysign(other : number) -> Tensor
Tensor.copysign(other : number,
out : Tensor) -> Tensor
Tensor.copysign_(other : Tensor) -> Tensor
Tensor.copysign_(other : number) -> Tensor
Tensor.corrcoef() -> Tensor
Tensor.cos() -> Tensor
Tensor.cos(out : Tensor) -> Tensor
Tensor.cos_() -> Tensor
Tensor.cosh() -> Tensor
Tensor.cosh(out : Tensor) -> Tensor
Tensor.cosh_() -> Tensor
Tensor.count_nonzero(dim : List[int]) -> Tensor
Tensor.count_nonzero(dim : Optional[int]) -> Tensor
Tensor.cov(correction : int=1,
fweights : Optional[Tensor],
aweights : Optional[Tensor]) -> Tensor
Tensor.cpu() -> Tensor
Tensor.cross(other : Tensor,
dim : Optional[int]) -> Tensor
Tensor.cross(other : Tensor,
dim : Optional[int],
out : Tensor) -> Tensor
Tensor.crow_indices() -> Tensor
Tensor.cuda() -> Tensor
Tensor.cummax(dim : int) -> Tuple[Tensor, Tensor]
Tensor.cummax(dim : str) -> Tuple[Tensor, Tensor]
Tensor.cummax(dim : str,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.cummax(dim : int,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.cummin(dim : int) -> Tuple[Tensor, Tensor]
Tensor.cummin(dim : str) -> Tuple[Tensor, Tensor]
Tensor.cummin(dim : str,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.cummin(dim : int,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.cumprod(dim : int,
dtype : Optional[int]) -> Tensor
Tensor.cumprod(dim : str,
dtype : Optional[int]) -> Tensor
Tensor.cumprod(dim : str,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.cumprod(dim : int,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.cumprod_(dim : int,
dtype : Optional[int]) -> Tensor
Tensor.cumprod_(dim : str,
dtype : Optional[int]) -> Tensor
Tensor.cumsum(dim : int,
dtype : Optional[int]) -> Tensor
Tensor.cumsum(dim : str,
dtype : Optional[int]) -> Tensor
Tensor.cumsum(dim : str,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.cumsum(dim : int,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.cumsum_(dim : int,
dtype : Optional[int]) -> Tensor
Tensor.cumsum_(dim : str,
dtype : Optional[int]) -> Tensor
Tensor.data() -> Tensor
Tensor.deg2rad() -> Tensor
Tensor.deg2rad(out : Tensor) -> Tensor
Tensor.deg2rad_() -> Tensor
Tensor.dense_dim() -> int
Tensor.dequantize() -> Tensor
Tensor.det() -> Tensor
Tensor.detach() -> Tensor
Tensor.detach_() -> Tensor
Tensor.diag(diagonal : int=0) -> Tensor
Tensor.diag(diagonal : int=0,
out : Tensor) -> Tensor
Tensor.diag_embed(offset : int=0,
dim1 : int=-2,
dim2 : int=-1) -> Tensor
Tensor.diagflat(offset : int=0) -> Tensor
Tensor.diagonal(offset : int=0,
dim1 : int=0,
dim2 : int=1) -> Tensor
Tensor.diagonal(outdim : str,
dim1 : str,
dim2 : str,
offset : int=0) -> Tensor
Tensor.diff(n : int=1,
dim : int=-1,
prepend : Optional[Tensor],
append : Optional[Tensor]) -> Tensor
Tensor.diff(n : int=1,
dim : int=-1,
prepend : Optional[Tensor],
append : Optional[Tensor],
out : Tensor) -> Tensor
Tensor.digamma() -> Tensor
Tensor.digamma(out : Tensor) -> Tensor
Tensor.digamma_() -> Tensor
Tensor.dim() -> int
Tensor.dist(other : Tensor,
p : number=2) -> Tensor
Tensor.div(other : Tensor) -> Tensor
Tensor.div(other : number) -> Tensor
Tensor.div(other : Tensor,
rounding_mode : Optional[str]) -> Tensor
Tensor.div(other : number,
rounding_mode : Optional[str]) -> Tensor
Tensor.div(other : Tensor,
out : Tensor) -> Tensor
Tensor.div(other : Tensor,
rounding_mode : Optional[str],
out : Tensor) -> Tensor
Tensor.div_(other : number) -> Tensor
Tensor.div_(other : Tensor) -> Tensor
Tensor.div_(other : Tensor,
rounding_mode : Optional[str]) -> Tensor
Tensor.div_(other : number,
rounding_mode : Optional[str]) -> Tensor
Tensor.divide(other : Tensor) -> Tensor
Tensor.divide(other : Tensor,
out : Tensor) -> Tensor
Tensor.divide(other : number) -> Tensor
Tensor.divide(other : Tensor,
rounding_mode : Optional[str]) -> Tensor
Tensor.divide(other : Tensor,
rounding_mode : Optional[str],
out : Tensor) -> Tensor
Tensor.divide(other : number,
rounding_mode : Optional[str]) -> Tensor
Tensor.divide_(other : Tensor) -> Tensor
Tensor.divide_(other : number) -> Tensor
Tensor.divide_(other : Tensor,
rounding_mode : Optional[str]) -> Tensor
Tensor.divide_(other : number,
rounding_mode : Optional[str]) -> Tensor
Tensor.dot(tensor : Tensor) -> Tensor
Tensor.dot(tensor : Tensor,
out : Tensor) -> Tensor
Tensor.dsplit(sections : int) -> List[Tensor]
Tensor.dsplit(indices : List[int]) -> List[Tensor]
Tensor.eig(eigenvectors : bool=False) -> Tuple[Tensor, Tensor]
Tensor.eig(eigenvectors : bool=False,
e : Tensor,
v : Tensor) -> Tuple[Tensor, Tensor]
Tensor.element_size() -> int
Tensor.eq(other : Tensor) -> Tensor
Tensor.eq(other : number) -> Tensor
Tensor.eq(other : number,
out : Tensor) -> Tensor
Tensor.eq(other : Tensor,
out : Tensor) -> Tensor
Tensor.eq_(other : number) -> Tensor
Tensor.eq_(other : Tensor) -> Tensor
Tensor.equal(other : Tensor) -> bool
Tensor.erf() -> Tensor
Tensor.erf(out : Tensor) -> Tensor
Tensor.erf_() -> Tensor
Tensor.erfc() -> Tensor
Tensor.erfc(out : Tensor) -> Tensor
Tensor.erfc_() -> Tensor
Tensor.erfinv() -> Tensor
Tensor.erfinv(out : Tensor) -> Tensor
Tensor.erfinv_() -> Tensor
Tensor.exp() -> Tensor
Tensor.exp(out : Tensor) -> Tensor
Tensor.exp2() -> Tensor
Tensor.exp2(out : Tensor) -> Tensor
Tensor.exp2_() -> Tensor
Tensor.exp_() -> Tensor
Tensor.expand(size : List[int],
implicit : bool=False) -> Tensor
Tensor.expand_as(other : Tensor) -> Tensor
Tensor.expm1() -> Tensor
Tensor.expm1(out : Tensor) -> Tensor
Tensor.expm1_() -> Tensor
Tensor.exponential_(lambd : float=1.0,
generator : Optional[Generator]) -> Tensor
Tensor.fill_(value : number) -> Tensor
Tensor.fill_(value : Tensor) -> Tensor
Tensor.fill_diagonal_(fill_value : number,
wrap : bool=False) -> Tensor
Tensor.fix() -> Tensor
Tensor.fix(out : Tensor) -> Tensor
Tensor.fix_() -> Tensor
Tensor.flatten(dims : List[str],
out_dim : str) -> Tensor
Tensor.flatten(start_dim : int,
end_dim : int,
out_dim : str) -> Tensor
Tensor.flatten(start_dim : int=0,
end_dim : int=-1) -> Tensor
Tensor.flatten(start_dim : str,
end_dim : str,
out_dim : str) -> Tensor
Tensor.flip(dims : List[int]) -> Tensor
Tensor.fliplr() -> Tensor
Tensor.flipud() -> Tensor
Tensor.float_power(exponent : Tensor) -> Tensor
Tensor.float_power(exponent : Tensor,
out : Tensor) -> Tensor
Tensor.float_power(exponent : number) -> Tensor
Tensor.float_power(exponent : number,
out : Tensor) -> Tensor
Tensor.float_power_(exponent : Tensor) -> Tensor
Tensor.float_power_(exponent : number) -> Tensor
Tensor.floor() -> Tensor
Tensor.floor(out : Tensor) -> Tensor
Tensor.floor_() -> Tensor
Tensor.floor_divide(other : Tensor) -> Tensor
Tensor.floor_divide(other : number) -> Tensor
Tensor.floor_divide(other : Tensor,
out : Tensor) -> Tensor
Tensor.floor_divide_(other : number) -> Tensor
Tensor.floor_divide_(other : Tensor) -> Tensor
Tensor.fmax(other : Tensor) -> Tensor
Tensor.fmax(other : Tensor,
out : Tensor) -> Tensor
Tensor.fmin(other : Tensor) -> Tensor
Tensor.fmin(other : Tensor,
out : Tensor) -> Tensor
Tensor.fmod(other : Tensor) -> Tensor
Tensor.fmod(other : number) -> Tensor
Tensor.fmod(other : Tensor,
out : Tensor) -> Tensor
Tensor.fmod(other : number,
out : Tensor) -> Tensor
Tensor.fmod_(other : Tensor) -> Tensor
Tensor.fmod_(other : number) -> Tensor
Tensor.frac() -> Tensor
Tensor.frac(out : Tensor) -> Tensor
Tensor.frac_() -> Tensor
Tensor.frexp(mantissa : Tensor,
exponent : Tensor) -> Tuple[Tensor, Tensor]
Tensor.frexp() -> Tuple[Tensor, Tensor]
Tensor.gather(dim : int,
index : Tensor,
sparse_grad : bool=False) -> Tensor
Tensor.gather(dim : int,
index : Tensor,
sparse_grad : bool=False,
out : Tensor) -> Tensor
Tensor.gather(dim : str,
index : Tensor,
sparse_grad : bool=False) -> Tensor
Tensor.gather(dim : str,
index : Tensor,
sparse_grad : bool=False,
out : Tensor) -> Tensor
Tensor.gcd(other : Tensor) -> Tensor
Tensor.gcd(other : Tensor,
out : Tensor) -> Tensor
Tensor.gcd_(other : Tensor) -> Tensor
Tensor.ge(other : Tensor) -> Tensor
Tensor.ge(other : number) -> Tensor
Tensor.ge(other : number,
out : Tensor) -> Tensor
Tensor.ge(other : Tensor,
out : Tensor) -> Tensor
Tensor.ge_(other : number) -> Tensor
Tensor.ge_(other : Tensor) -> Tensor
Tensor.geometric_(p : float,
generator : Optional[Generator]) -> Tensor
Tensor.geqrf() -> Tuple[Tensor, Tensor]
Tensor.geqrf(a : Tensor,
tau : Tensor) -> Tuple[Tensor, Tensor]
Tensor.ger(vec2 : Tensor) -> Tensor
Tensor.ger(vec2 : Tensor,
out : Tensor) -> Tensor
Tensor.get_device() -> int
Tensor.greater(other : number) -> Tensor
Tensor.greater(other : number,
out : Tensor) -> Tensor
Tensor.greater(other : Tensor) -> Tensor
Tensor.greater(other : Tensor,
out : Tensor) -> Tensor
Tensor.greater_(other : number) -> Tensor
Tensor.greater_(other : Tensor) -> Tensor
Tensor.greater_equal(other : number) -> Tensor
Tensor.greater_equal(other : number,
out : Tensor) -> Tensor
Tensor.greater_equal(other : Tensor) -> Tensor
Tensor.greater_equal(other : Tensor,
out : Tensor) -> Tensor
Tensor.greater_equal_(other : number) -> Tensor
Tensor.greater_equal_(other : Tensor) -> Tensor
Tensor.gt(other : Tensor) -> Tensor
Tensor.gt(other : number) -> Tensor
Tensor.gt(other : number,
out : Tensor) -> Tensor
Tensor.gt(other : Tensor,
out : Tensor) -> Tensor
Tensor.gt_(other : number) -> Tensor
Tensor.gt_(other : Tensor) -> Tensor
Tensor.hardshrink(lambd : number=0.5) -> Tensor
Tensor.hardshrink(lambd : number=0.5,
out : Tensor) -> Tensor
Tensor.heaviside(values : Tensor) -> Tensor
Tensor.heaviside(values : Tensor,
out : Tensor) -> Tensor
Tensor.heaviside_(values : Tensor) -> Tensor
Tensor.histc(bins : int=100,
min : number=0,
max : number=0) -> Tensor
Tensor.histc(bins : int=100,
min : number=0,
max : number=0,
out : Tensor) -> Tensor
Tensor.histogram(bins : Tensor,
weight : Optional[Tensor],
density : bool=False) -> Tuple[Tensor, Tensor]
Tensor.histogram(bins : Tensor,
weight : Optional[Tensor],
density : bool=False,
hist : Tensor,
bin_edges : Tensor) -> Tuple[Tensor, Tensor]
Tensor.histogram(bins : int=100,
range : Optional[List[float]],
weight : Optional[Tensor],
density : bool=False) -> Tuple[Tensor, Tensor]
Tensor.histogram(bins : int=100,
range : Optional[List[float]],
weight : Optional[Tensor],
density : bool=False,
hist : Tensor,
bin_edges : Tensor) -> Tuple[Tensor, Tensor]
Tensor.hsplit(sections : int) -> List[Tensor]
Tensor.hsplit(indices : List[int]) -> List[Tensor]
Tensor.hypot(other : Tensor) -> Tensor
Tensor.hypot(other : Tensor,
out : Tensor) -> Tensor
Tensor.hypot_(other : Tensor) -> Tensor
Tensor.i0() -> Tensor
Tensor.i0(out : Tensor) -> Tensor
Tensor.i0_() -> Tensor
Tensor.igamma(other : Tensor) -> Tensor
Tensor.igamma(other : Tensor,
out : Tensor) -> Tensor
Tensor.igamma_(other : Tensor) -> Tensor
Tensor.igammac(other : Tensor) -> Tensor
Tensor.igammac(other : Tensor,
out : Tensor) -> Tensor
Tensor.igammac_(other : Tensor) -> Tensor
Tensor.imag() -> Tensor
Tensor.index_add(dim : int,
index : Tensor,
source : Tensor) -> Tensor
Tensor.index_add(dim : int,
index : Tensor,
source : Tensor,
alpha : number) -> Tensor
Tensor.index_add(dim : str,
index : Tensor,
source : Tensor,
alpha : number=1) -> Tensor
Tensor.index_add_(dim : int,
index : Tensor,
source : Tensor,
alpha : number) -> Tensor
Tensor.index_add_(dim : int,
index : Tensor,
source : Tensor) -> Tensor
Tensor.index_copy(dim : int,
index : Tensor,
source : Tensor) -> Tensor
Tensor.index_copy(dim : str,
index : Tensor,
source : Tensor) -> Tensor
Tensor.index_copy_(dim : int,
index : Tensor,
source : Tensor) -> Tensor
Tensor.index_copy_(dim : str,
index : Tensor,
source : Tensor) -> Tensor
Tensor.index_fill(dim : str,
index : Tensor,
value : number) -> Tensor
Tensor.index_fill(dim : str,
index : Tensor,
value : Tensor) -> Tensor
Tensor.index_fill(dim : int,
index : Tensor,
value : number) -> Tensor
Tensor.index_fill(dim : int,
index : Tensor,
value : Tensor) -> Tensor
Tensor.index_fill_(dim : str,
index : Tensor,
value : number) -> Tensor
Tensor.index_fill_(dim : str,
index : Tensor,
value : Tensor) -> Tensor
Tensor.index_fill_(dim : int,
index : Tensor,
value : number) -> Tensor
Tensor.index_fill_(dim : int,
index : Tensor,
value : Tensor) -> Tensor
Tensor.index_put(indices : List[Optional[Tensor]],
values : Tensor,
accumulate : bool=False) -> Tensor
Tensor.index_put(indices : List[Tensor],
values : Tensor,
accumulate : bool=False) -> Tensor
Tensor.index_put_(indices : List[Optional[Tensor]],
values : Tensor,
accumulate : bool=False) -> Tensor
Tensor.index_put_(indices : List[Tensor],
values : Tensor,
accumulate : bool=False) -> Tensor
Tensor.index_select(dim : int,
index : Tensor) -> Tensor
Tensor.index_select(dim : int,
index : Tensor,
out : Tensor) -> Tensor
Tensor.index_select(dim : str,
index : Tensor) -> Tensor
Tensor.index_select(dim : str,
index : Tensor,
out : Tensor) -> Tensor
Tensor.indices() -> Tensor
Tensor.inner(other : Tensor) -> Tensor
Tensor.inner(other : Tensor,
out : Tensor) -> Tensor
Tensor.int_repr() -> Tensor
Tensor.inverse() -> Tensor
Tensor.inverse(out : Tensor) -> Tensor
Tensor.is_coalesced() -> bool
Tensor.is_complex() -> bool
Tensor.is_conj() -> bool
Tensor.is_contiguous() -> bool
Tensor.is_distributed() -> bool
Tensor.is_floating_point() -> bool
Tensor.is_inference() -> bool
Tensor.is_leaf() -> bool
Tensor.is_neg() -> bool
Tensor.is_nonzero() -> bool
Tensor.is_pinned(device : Optional[Device]) -> bool
Tensor.is_same_size(other : Tensor) -> bool
Tensor.is_set_to(tensor : Tensor) -> bool
Tensor.is_signed() -> bool
Tensor.isclose(other : Tensor,
rtol : float=1e-05,
atol : float=1e-08,
equal_nan : bool=False) -> Tensor
Tensor.isfinite() -> Tensor
Tensor.isinf() -> Tensor
Tensor.isnan() -> Tensor
Tensor.isneginf() -> Tensor
Tensor.isneginf(out : Tensor) -> Tensor
Tensor.isposinf() -> Tensor
Tensor.isposinf(out : Tensor) -> Tensor
Tensor.isreal() -> Tensor
Tensor.istft(n_fft : int,
hop_length : Optional[int],
win_length : Optional[int],
window : Optional[Tensor],
center : bool=True,
normalized : bool=False,
onesided : Optional[bool],
length : Optional[int],
return_complex : bool=False) -> Tensor
Tensor.item() -> number
Tensor.kron(other : Tensor) -> Tensor
Tensor.kron(other : Tensor,
out : Tensor) -> Tensor
Tensor.kthvalue(k : int,
dim : int=-1,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.kthvalue(k : int,
dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.kthvalue(k : int,
dim : str,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.kthvalue(k : int,
dim : int=-1,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.lcm(other : Tensor) -> Tensor
Tensor.lcm(other : Tensor,
out : Tensor) -> Tensor
Tensor.lcm_(other : Tensor) -> Tensor
Tensor.ldexp(other : Tensor) -> Tensor
Tensor.ldexp(other : Tensor,
out : Tensor) -> Tensor
Tensor.ldexp_(other : Tensor) -> Tensor
Tensor.le(other : Tensor) -> Tensor
Tensor.le(other : number) -> Tensor
Tensor.le(other : number,
out : Tensor) -> Tensor
Tensor.le(other : Tensor,
out : Tensor) -> Tensor
Tensor.le_(other : number) -> Tensor
Tensor.le_(other : Tensor) -> Tensor
Tensor.lerp(end : Tensor,
weight : number) -> Tensor
Tensor.lerp(end : Tensor,
weight : number,
out : Tensor) -> Tensor
Tensor.lerp(end : Tensor,
weight : Tensor) -> Tensor
Tensor.lerp(end : Tensor,
weight : Tensor,
out : Tensor) -> Tensor
Tensor.lerp_(end : Tensor,
weight : number) -> Tensor
Tensor.lerp_(end : Tensor,
weight : Tensor) -> Tensor
Tensor.less(other : number) -> Tensor
Tensor.less(other : number,
out : Tensor) -> Tensor
Tensor.less(other : Tensor) -> Tensor
Tensor.less(other : Tensor,
out : Tensor) -> Tensor
Tensor.less_(other : number) -> Tensor
Tensor.less_(other : Tensor) -> Tensor
Tensor.less_equal(other : number) -> Tensor
Tensor.less_equal(other : number,
out : Tensor) -> Tensor
Tensor.less_equal(other : Tensor) -> Tensor
Tensor.less_equal(other : Tensor,
out : Tensor) -> Tensor
Tensor.less_equal_(other : number) -> Tensor
Tensor.less_equal_(other : Tensor) -> Tensor
Tensor.lgamma() -> Tensor
Tensor.lgamma(out : Tensor) -> Tensor
Tensor.lgamma_() -> Tensor
Tensor.log() -> Tensor
Tensor.log(out : Tensor) -> Tensor
Tensor.log10() -> Tensor
Tensor.log10(out : Tensor) -> Tensor
Tensor.log10_() -> Tensor
Tensor.log1p() -> Tensor
Tensor.log1p(out : Tensor) -> Tensor
Tensor.log1p_() -> Tensor
Tensor.log2() -> Tensor
Tensor.log2(out : Tensor) -> Tensor
Tensor.log2_() -> Tensor
Tensor.log_() -> Tensor
Tensor.log_normal_(mean : float=1.0,
std : float=2.0,
generator : Optional[Generator]) -> Tensor
Tensor.log_softmax(dim : int,
dtype : Optional[int]) -> Tensor
Tensor.log_softmax(dim : str,
dtype : Optional[int]) -> Tensor
Tensor.logaddexp(other : Tensor) -> Tensor
Tensor.logaddexp(other : Tensor,
out : Tensor) -> Tensor
Tensor.logaddexp2(other : Tensor) -> Tensor
Tensor.logaddexp2(other : Tensor,
out : Tensor) -> Tensor
Tensor.logcumsumexp(dim : int) -> Tensor
Tensor.logcumsumexp(dim : str) -> Tensor
Tensor.logcumsumexp(dim : str,
out : Tensor) -> Tensor
Tensor.logcumsumexp(dim : int,
out : Tensor) -> Tensor
Tensor.logdet() -> Tensor
Tensor.logical_and(other : Tensor) -> Tensor
Tensor.logical_and(other : Tensor,
out : Tensor) -> Tensor
Tensor.logical_and_(other : Tensor) -> Tensor
Tensor.logical_not() -> Tensor
Tensor.logical_not(out : Tensor) -> Tensor
Tensor.logical_not_() -> Tensor
Tensor.logical_or(other : Tensor) -> Tensor
Tensor.logical_or(other : Tensor,
out : Tensor) -> Tensor
Tensor.logical_or_(other : Tensor) -> Tensor
Tensor.logical_xor(other : Tensor) -> Tensor
Tensor.logical_xor(other : Tensor,
out : Tensor) -> Tensor
Tensor.logical_xor_(other : Tensor) -> Tensor
Tensor.logit(eps : Optional[float]) -> Tensor
Tensor.logit(eps : Optional[float],
out : Tensor) -> Tensor
Tensor.logit_(eps : Optional[float]) -> Tensor
Tensor.logsumexp(dim : List[int],
keepdim : bool=False) -> Tensor
Tensor.logsumexp(dim : List[str],
keepdim : bool=False) -> Tensor
Tensor.logsumexp(dim : List[str],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.logsumexp(dim : List[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.lstsq(A : Tensor) -> Tuple[Tensor, Tensor]
Tensor.lstsq(A : Tensor,
X : Tensor,
qr : Tensor) -> Tuple[Tensor, Tensor]
Tensor.lt(other : Tensor) -> Tensor
Tensor.lt(other : number) -> Tensor
Tensor.lt(other : number,
out : Tensor) -> Tensor
Tensor.lt(other : Tensor,
out : Tensor) -> Tensor
Tensor.lt_(other : number) -> Tensor
Tensor.lt_(other : Tensor) -> Tensor
Tensor.lu_solve(LU_data : Tensor,
LU_pivots : Tensor) -> Tensor
Tensor.lu_solve(LU_data : Tensor,
LU_pivots : Tensor,
out : Tensor) -> Tensor
Tensor.masked_fill(mask : Tensor,
value : number) -> Tensor
Tensor.masked_fill(mask : Tensor,
value : Tensor) -> Tensor
Tensor.masked_fill_(mask : Tensor,
value : number) -> Tensor
Tensor.masked_fill_(mask : Tensor,
value : Tensor) -> Tensor
Tensor.masked_scatter(mask : Tensor,
source : Tensor) -> Tensor
Tensor.masked_scatter_(mask : Tensor,
source : Tensor) -> Tensor
Tensor.masked_select(mask : Tensor) -> Tensor
Tensor.masked_select(mask : Tensor,
out : Tensor) -> Tensor
Tensor.matmul(other : Tensor) -> Tensor
Tensor.matmul(other : Tensor,
out : Tensor) -> Tensor
Tensor.matrix_exp() -> Tensor
Tensor.matrix_power(n : int) -> Tensor
Tensor.matrix_power(n : int,
out : Tensor) -> Tensor
Tensor.max() -> Tensor
Tensor.max(dim : int,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.max(dim : int,
keepdim : bool=False,
max : Tensor,
max_values : Tensor) -> Tuple[Tensor, Tensor]
Tensor.max(dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.max(dim : str,
keepdim : bool=False,
max : Tensor,
max_values : Tensor) -> Tuple[Tensor, Tensor]
Tensor.max(other : Tensor) -> Tensor
Tensor.max(other : Tensor,
out : Tensor) -> Tensor
Tensor.maximum(other : Tensor) -> Tensor
Tensor.maximum(other : Tensor,
out : Tensor) -> Tensor
Tensor.mean(dtype : Optional[int]) -> Tensor
Tensor.mean(dim : List[int],
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
Tensor.mean(dim : List[str],
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
Tensor.mean(dim : List[str],
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.mean(dim : List[int],
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.median() -> Tensor
Tensor.median(dim : int,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.median(dim : int,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.median(dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.median(dim : str,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.min() -> Tensor
Tensor.min(dim : int,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.min(dim : int,
keepdim : bool=False,
min : Tensor,
min_indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.min(dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.min(dim : str,
keepdim : bool=False,
min : Tensor,
min_indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.min(other : Tensor) -> Tensor
Tensor.min(other : Tensor,
out : Tensor) -> Tensor
Tensor.minimum(other : Tensor) -> Tensor
Tensor.minimum(other : Tensor,
out : Tensor) -> Tensor
Tensor.mm(mat2 : Tensor) -> Tensor
Tensor.mm(mat2 : Tensor,
out : Tensor) -> Tensor
Tensor.mode(dim : int=-1,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.mode(dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.mode(dim : str,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.mode(dim : int=-1,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.moveaxis(source : List[int],
destination : List[int]) -> Tensor
Tensor.moveaxis(source : int,
destination : int) -> Tensor
Tensor.movedim(source : List[int],
destination : List[int]) -> Tensor
Tensor.movedim(source : int,
destination : int) -> Tensor
Tensor.msort() -> Tensor
Tensor.msort(out : Tensor) -> Tensor
Tensor.mul(other : Tensor) -> Tensor
Tensor.mul(other : number) -> Tensor
Tensor.mul(other : Tensor,
out : Tensor) -> Tensor
Tensor.mul_(other : Tensor) -> Tensor
Tensor.mul_(other : number) -> Tensor
Tensor.multinomial(num_samples : int,
replacement : bool=False,
generator : Optional[Generator]) -> Tensor
Tensor.multinomial(num_samples : int,
replacement : bool=False,
generator : Optional[Generator],
out : Tensor) -> Tensor
Tensor.multiply(other : Tensor) -> Tensor
Tensor.multiply(other : Tensor,
out : Tensor) -> Tensor
Tensor.multiply(other : number) -> Tensor
Tensor.multiply_(other : Tensor) -> Tensor
Tensor.multiply_(other : number) -> Tensor
Tensor.mv(vec : Tensor) -> Tensor
Tensor.mv(vec : Tensor,
out : Tensor) -> Tensor
Tensor.mvlgamma(p : int,
out : Tensor) -> Tensor
Tensor.mvlgamma(p : int) -> Tensor
Tensor.mvlgamma_(p : int) -> Tensor
Tensor.nan_to_num(nan : Optional[float],
posinf : Optional[float],
neginf : Optional[float],
out : Tensor) -> Tensor
Tensor.nan_to_num(nan : Optional[float],
posinf : Optional[float],
neginf : Optional[float]) -> Tensor
Tensor.nan_to_num_(nan : Optional[float],
posinf : Optional[float],
neginf : Optional[float]) -> Tensor
Tensor.nanmean(dim : List[int]=[],
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
Tensor.nanmean(dim : List[int]=[],
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.nanmedian() -> Tensor
Tensor.nanmedian(dim : int,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.nanmedian(dim : int,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.nanmedian(dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
Tensor.nanmedian(dim : str,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.nanquantile(q : Tensor,
dim : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.nanquantile(q : float,
dim : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.nanquantile(q : Tensor,
dim : Optional[int],
keepdim : bool,
interpolation : str) -> Tensor
Tensor.nanquantile(q : float,
dim : Optional[int],
keepdim : bool,
interpolation : str) -> Tensor
Tensor.nanquantile(q : float,
dim : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.nanquantile(q : Tensor,
dim : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.nanquantile(q : float,
dim : Optional[int],
keepdim : bool,
interpolation : str,
out : Tensor) -> Tensor
Tensor.nanquantile(q : Tensor,
dim : Optional[int],
keepdim : bool,
interpolation : str,
out : Tensor) -> Tensor
Tensor.nansum(dtype : Optional[int]) -> Tensor
Tensor.nansum(dim : List[int],
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
Tensor.nansum(dim : List[int],
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.narrow(dim : int,
start : int,
length : int) -> Tensor
Tensor.narrow(dim : int,
start : Tensor,
length : int) -> Tensor
Tensor.narrow_copy(dim : int,
start : int,
length : int) -> Tensor
Tensor.narrow_copy(dim : int,
start : int,
length : int,
out : Tensor) -> Tensor
Tensor.ne(other : Tensor) -> Tensor
Tensor.ne(other : number) -> Tensor
Tensor.ne(other : number,
out : Tensor) -> Tensor
Tensor.ne(other : Tensor,
out : Tensor) -> Tensor
Tensor.ne_(other : number) -> Tensor
Tensor.ne_(other : Tensor) -> Tensor
Tensor.neg() -> Tensor
Tensor.neg(out : Tensor) -> Tensor
Tensor.neg_() -> Tensor
Tensor.negative() -> Tensor
Tensor.negative(out : Tensor) -> Tensor
Tensor.negative_() -> Tensor
Tensor.new_empty(size : List[int],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
Tensor.new_empty_strided(size : List[int],
stride : List[int],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
Tensor.new_full(size : List[int],
fill_value : number,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
Tensor.new_ones(size : List[int],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
Tensor.new_zeros(size : List[int],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
Tensor.nextafter(other : Tensor) -> Tensor
Tensor.nextafter(other : Tensor,
out : Tensor) -> Tensor
Tensor.nextafter_(other : Tensor) -> Tensor
Tensor.nonzero() -> Tensor
Tensor.nonzero(out : Tensor) -> Tensor
Tensor.norm(p : number=2) -> Tensor
Tensor.norm(p : Optional[number],
dim : List[int],
keepdim : bool=False) -> Tensor
Tensor.norm(p : Optional[number],
dim : List[str],
keepdim : bool=False) -> Tensor
Tensor.norm(p : Optional[number],
dim : List[int],
keepdim : bool,
dtype : int) -> Tensor
Tensor.norm(p : Optional[number],
dim : List[int],
keepdim : bool,
dtype : int,
out : Tensor) -> Tensor
Tensor.norm(p : Optional[number],
dim : List[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.norm(p : Optional[number],
dtype : int) -> Tensor
Tensor.norm(p : Optional[number],
dim : List[str],
keepdim : bool,
dtype : int) -> Tensor
Tensor.norm(p : Optional[number],
dim : List[str],
keepdim : bool,
dtype : int,
out : Tensor) -> Tensor
Tensor.norm(p : Optional[number],
dim : List[str],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.normal_(mean : float=0.0,
std : float=1.0,
generator : Optional[Generator]) -> Tensor
Tensor.not_equal(other : number) -> Tensor
Tensor.not_equal(other : number,
out : Tensor) -> Tensor
Tensor.not_equal(other : Tensor) -> Tensor
Tensor.not_equal(other : Tensor,
out : Tensor) -> Tensor
Tensor.not_equal_(other : number) -> Tensor
Tensor.not_equal_(other : Tensor) -> Tensor
Tensor.numel() -> int
Tensor.orgqr(input2 : Tensor) -> Tensor
Tensor.orgqr(input2 : Tensor,
out : Tensor) -> Tensor
Tensor.ormqr(input2 : Tensor,
input3 : Tensor,
left : bool=True,
transpose : bool=False) -> Tensor
Tensor.ormqr(input2 : Tensor,
input3 : Tensor,
left : bool=True,
transpose : bool=False,
out : Tensor) -> Tensor
Tensor.outer(vec2 : Tensor) -> Tensor
Tensor.outer(vec2 : Tensor,
out : Tensor) -> Tensor
Tensor.output_nr() -> int
Tensor.permute(dims : List[int]) -> Tensor
Tensor.pin_memory(device : Optional[Device]) -> Tensor
Tensor.pinverse(rcond : float=1e-15) -> Tensor
Tensor.polygamma_(n : int) -> Tensor
Tensor.positive() -> Tensor
Tensor.pow(exponent : Tensor) -> Tensor
Tensor.pow(exponent : number) -> Tensor
Tensor.pow(exponent : number,
out : Tensor) -> Tensor
Tensor.pow(exponent : Tensor,
out : Tensor) -> Tensor
Tensor.pow_(exponent : number) -> Tensor
Tensor.pow_(exponent : Tensor) -> Tensor
Tensor.prelu(weight : Tensor) -> Tensor
Tensor.prod(dtype : Optional[int]) -> Tensor
Tensor.prod(dim : int,
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
Tensor.prod(dim : str,
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
Tensor.prod(dim : str,
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.prod(dim : int,
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.put(index : Tensor,
source : Tensor,
accumulate : bool=False) -> Tensor
Tensor.put_(index : Tensor,
source : Tensor,
accumulate : bool=False) -> Tensor
Tensor.q_per_channel_axis() -> int
Tensor.q_per_channel_scales() -> Tensor
Tensor.q_per_channel_zero_points() -> Tensor
Tensor.q_scale() -> float
Tensor.q_zero_point() -> int
Tensor.qr(some : bool=True) -> Tuple[Tensor, Tensor]
Tensor.qr(some : bool=True,
Q : Tensor,
R : Tensor) -> Tuple[Tensor, Tensor]
Tensor.qscheme() -> QScheme
Tensor.quantile(q : Tensor,
dim : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.quantile(q : float,
dim : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.quantile(q : Tensor,
dim : Optional[int],
keepdim : bool,
interpolation : str) -> Tensor
Tensor.quantile(q : float,
dim : Optional[int],
keepdim : bool,
interpolation : str) -> Tensor
Tensor.quantile(q : float,
dim : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.quantile(q : Tensor,
dim : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.quantile(q : float,
dim : Optional[int],
keepdim : bool,
interpolation : str,
out : Tensor) -> Tensor
Tensor.quantile(q : Tensor,
dim : Optional[int],
keepdim : bool,
interpolation : str,
out : Tensor) -> Tensor
Tensor.rad2deg() -> Tensor
Tensor.rad2deg(out : Tensor) -> Tensor
Tensor.rad2deg_() -> Tensor
Tensor.random_(from : int,
to : Optional[int],
generator : Optional[Generator]) -> Tensor
Tensor.random_(to : int,
generator : Optional[Generator]) -> Tensor
Tensor.random_(generator : Optional[Generator]) -> Tensor
Tensor.ravel() -> Tensor
Tensor.real() -> Tensor
Tensor.reciprocal() -> Tensor
Tensor.reciprocal(out : Tensor) -> Tensor
Tensor.reciprocal_() -> Tensor
Tensor.record_stream(s : Stream) -> Tuple[]
Tensor.refine_names(names : List[str]) -> Tensor
Tensor.relu() -> Tensor
Tensor.relu_() -> Tensor
Tensor.remainder(other : Tensor) -> Tensor
Tensor.remainder(other : Tensor,
out : Tensor) -> Tensor
Tensor.remainder(other : number) -> Tensor
Tensor.remainder(other : number,
out : Tensor) -> Tensor
Tensor.remainder_(other : Tensor) -> Tensor
Tensor.remainder_(other : number) -> Tensor
Tensor.rename(names : Optional[List[str]]) -> Tensor
Tensor.rename_(names : Optional[List[str]]) -> Tensor
Tensor.renorm(p : number,
dim : int,
maxnorm : number) -> Tensor
Tensor.renorm(p : number,
dim : int,
maxnorm : number,
out : Tensor) -> Tensor
Tensor.renorm_(p : number,
dim : int,
maxnorm : number) -> Tensor
Tensor.repeat(repeats : List[int]) -> Tensor
Tensor.repeat_interleave(repeats : Tensor,
dim : Optional[int],
output_size : Optional[int]) -> Tensor
Tensor.repeat_interleave(repeats : int,
dim : Optional[int],
output_size : Optional[int]) -> Tensor
Tensor.requires_grad_(requires_grad : bool=True) -> Tensor
Tensor.reshape(shape : List[int]) -> Tensor
Tensor.reshape_as(other : Tensor) -> Tensor
Tensor.resize_(size : List[int],
memory_format : Optional[int]) -> Tensor
Tensor.resize_as_(the_template : Tensor,
memory_format : Optional[int]) -> Tensor
Tensor.resolve_conj() -> Tensor
Tensor.resolve_neg() -> Tensor
Tensor.retain_grad() -> Tuple[]
Tensor.retains_grad() -> bool
Tensor.roll(shifts : List[int],
dims : List[int]=[]) -> Tensor
Tensor.rot90(k : int=1,
dims : List[int]=[0, 1]) -> Tensor
Tensor.round() -> Tensor
Tensor.round(out : Tensor) -> Tensor
Tensor.round_() -> Tensor
Tensor.rsqrt() -> Tensor
Tensor.rsqrt(out : Tensor) -> Tensor
Tensor.rsqrt_() -> Tensor
Tensor.scatter(dim : int,
index : Tensor,
src : Tensor) -> Tensor
Tensor.scatter(dim : int,
index : Tensor,
src : Tensor,
out : Tensor) -> Tensor
Tensor.scatter(dim : int,
index : Tensor,
value : number) -> Tensor
Tensor.scatter(dim : int,
index : Tensor,
value : number,
out : Tensor) -> Tensor
Tensor.scatter(dim : int,
index : Tensor,
src : Tensor,
reduce : str) -> Tensor
Tensor.scatter(dim : int,
index : Tensor,
src : Tensor,
reduce : str,
out : Tensor) -> Tensor
Tensor.scatter(dim : int,
index : Tensor,
value : number,
reduce : str) -> Tensor
Tensor.scatter(dim : int,
index : Tensor,
value : number,
reduce : str,
out : Tensor) -> Tensor
Tensor.scatter(dim : str,
index : Tensor,
src : Tensor) -> Tensor
Tensor.scatter(dim : str,
index : Tensor,
value : number) -> Tensor
Tensor.scatter_(dim : int,
index : Tensor,
src : Tensor) -> Tensor
Tensor.scatter_(dim : int,
index : Tensor,
value : number) -> Tensor
Tensor.scatter_(dim : int,
index : Tensor,
src : Tensor,
reduce : str) -> Tensor
Tensor.scatter_(dim : int,
index : Tensor,
value : number,
reduce : str) -> Tensor
Tensor.scatter_add(dim : int,
index : Tensor,
src : Tensor) -> Tensor
Tensor.scatter_add(dim : int,
index : Tensor,
src : Tensor,
out : Tensor) -> Tensor
Tensor.scatter_add(dim : str,
index : Tensor,
src : Tensor) -> Tensor
Tensor.scatter_add_(dim : int,
index : Tensor,
src : Tensor) -> Tensor
Tensor.select(dim : int,
index : int) -> Tensor
Tensor.select(dim : str,
index : int) -> Tensor
Tensor.set_(source : Storage,
storage_offset : int,
size : List[int],
stride : List[int]=[]) -> Tensor
Tensor.set_(source : Tensor) -> Tensor
Tensor.set_() -> Tensor
Tensor.set_(source : Storage) -> Tensor
Tensor.sgn() -> Tensor
Tensor.sgn(out : Tensor) -> Tensor
Tensor.sgn_() -> Tensor
Tensor.sigmoid() -> Tensor
Tensor.sigmoid(out : Tensor) -> Tensor
Tensor.sigmoid_() -> Tensor
Tensor.sign() -> Tensor
Tensor.sign(out : Tensor) -> Tensor
Tensor.sign_() -> Tensor
Tensor.signbit() -> Tensor
Tensor.signbit(out : Tensor) -> Tensor
Tensor.sin() -> Tensor
Tensor.sin(out : Tensor) -> Tensor
Tensor.sin_() -> Tensor
Tensor.sinc() -> Tensor
Tensor.sinc(out : Tensor) -> Tensor
Tensor.sinc_() -> Tensor
Tensor.sinh() -> Tensor
Tensor.sinh(out : Tensor) -> Tensor
Tensor.sinh_() -> Tensor
Tensor.size(dim : int) -> int
Tensor.size(dim : str) -> int
Tensor.size() -> List[int]
Tensor.slogdet() -> Tuple[Tensor, Tensor]
Tensor.smm(mat2 : Tensor) -> Tensor
Tensor.softmax(dim : int,
dtype : Optional[int]) -> Tensor
Tensor.softmax(dim : str,
dtype : Optional[int]) -> Tensor
Tensor.solve(A : Tensor) -> Tuple[Tensor, Tensor]
Tensor.solve(A : Tensor,
solution : Tensor,
lu : Tensor) -> Tuple[Tensor, Tensor]
Tensor.sort(dim : int=-1,
descending : bool=False) -> Tuple[Tensor, Tensor]
Tensor.sort(dim : int=-1,
descending : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.sort(stable : Optional[bool],
dim : int=-1,
descending : bool=False) -> Tuple[Tensor, Tensor]
Tensor.sort(stable : Optional[bool],
dim : int=-1,
descending : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.sort(dim : str,
descending : bool=False) -> Tuple[Tensor, Tensor]
Tensor.sort(dim : str,
descending : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.sort(stable : Optional[bool],
dim : str,
descending : bool=False) -> Tuple[Tensor, Tensor]
Tensor.sort(stable : Optional[bool],
dim : str,
descending : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.sparse_dim() -> int
Tensor.sparse_mask(mask : Tensor) -> Tensor
Tensor.sparse_resize_(size : List[int],
sparse_dim : int,
dense_dim : int) -> Tensor
Tensor.sparse_resize_and_clear_(size : List[int],
sparse_dim : int,
dense_dim : int) -> Tensor
Tensor.split(split_size : int,
dim : int=0) -> List[Tensor]
Tensor.split(split_sizes : List[int],
dim : int=0) -> List[Tensor]
Tensor.split_with_sizes(split_sizes : List[int],
dim : int=0) -> List[Tensor]
Tensor.sqrt() -> Tensor
Tensor.sqrt(out : Tensor) -> Tensor
Tensor.sqrt_() -> Tensor
Tensor.square() -> Tensor
Tensor.square(out : Tensor) -> Tensor
Tensor.square_() -> Tensor
Tensor.squeeze() -> Tensor
Tensor.squeeze(dim : int) -> Tensor
Tensor.squeeze(dim : str) -> Tensor
Tensor.squeeze_() -> Tensor
Tensor.squeeze_(dim : int) -> Tensor
Tensor.squeeze_(dim : str) -> Tensor
Tensor.sspaddmm(mat1 : Tensor,
mat2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.sspaddmm(mat1 : Tensor,
mat2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
Tensor.std(unbiased : bool=True) -> Tensor
Tensor.std(dim : List[int],
unbiased : bool=True,
keepdim : bool=False) -> Tensor
Tensor.std(dim : List[str],
unbiased : bool=True,
keepdim : bool=False) -> Tensor
Tensor.std(dim : List[str],
unbiased : bool=True,
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.std(dim : List[int],
unbiased : bool=True,
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.std(dim : Optional[List[int]],
correction : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.std(dim : Optional[List[int]],
correction : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.std(dim : List[str],
correction : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.std(dim : List[str],
correction : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.stft(n_fft : int,
hop_length : Optional[int],
win_length : Optional[int],
window : Optional[Tensor],
normalized : bool=False,
onesided : Optional[bool],
return_complex : Optional[bool]) -> Tensor
Tensor.storage_offset() -> int
Tensor.stride(dim : int) -> int
Tensor.stride(dim : str) -> int
Tensor.sub(other : Tensor,
alpha : number=1) -> Tensor
Tensor.sub(other : number,
alpha : number=1) -> Tensor
Tensor.sub(other : Tensor,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.sub_(other : number,
alpha : number=1) -> Tensor
Tensor.sub_(other : Tensor,
alpha : number=1) -> Tensor
Tensor.subtract(other : Tensor,
alpha : number=1) -> Tensor
Tensor.subtract(other : Tensor,
alpha : number=1,
out : Tensor) -> Tensor
Tensor.subtract(other : number,
alpha : number=1) -> Tensor
Tensor.subtract_(other : Tensor,
alpha : number=1) -> Tensor
Tensor.subtract_(other : number,
alpha : number=1) -> Tensor
Tensor.sum(dim : List[int],
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
Tensor.sum(dtype : Optional[int]) -> Tensor
Tensor.sum(dim : List[str],
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
Tensor.sum(dim : List[str],
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.sum(dim : List[int],
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
Tensor.sum_to_size(size : List[int]) -> Tensor
Tensor.svd(some : bool=True,
compute_uv : bool=True) -> Tuple[Tensor, Tensor, Tensor]
Tensor.svd(some : bool=True,
compute_uv : bool=True,
U : Tensor,
S : Tensor,
V : Tensor) -> Tuple[Tensor, Tensor, Tensor]
Tensor.swapaxes(axis0 : int,
axis1 : int) -> Tensor
Tensor.swapaxes_(axis0 : int,
axis1 : int) -> Tensor
Tensor.swapdims(dim0 : int,
dim1 : int) -> Tensor
Tensor.swapdims_(dim0 : int,
dim1 : int) -> Tensor
Tensor.symeig(eigenvectors : bool=False,
upper : bool=True) -> Tuple[Tensor, Tensor]
Tensor.symeig(eigenvectors : bool=False,
upper : bool=True,
e : Tensor,
V : Tensor) -> Tuple[Tensor, Tensor]
Tensor.t() -> Tensor
Tensor.t_() -> Tensor
Tensor.take(index : Tensor) -> Tensor
Tensor.take(index : Tensor,
out : Tensor) -> Tensor
Tensor.take_along_dim(indices : Tensor,
dim : Optional[int]) -> Tensor
Tensor.take_along_dim(indices : Tensor,
dim : Optional[int],
out : Tensor) -> Tensor
Tensor.tan() -> Tensor
Tensor.tan(out : Tensor) -> Tensor
Tensor.tan_() -> Tensor
Tensor.tanh() -> Tensor
Tensor.tanh(out : Tensor) -> Tensor
Tensor.tanh_() -> Tensor
Tensor.tensor_split(sections : int,
dim : int=0) -> List[Tensor]
Tensor.tensor_split(indices : List[int],
dim : int=0) -> List[Tensor]
Tensor.tensor_split(tensor_indices_or_sections : Tensor,
dim : int=0) -> List[Tensor]
Tensor.tile(dims : List[int]) -> Tensor
Tensor.to(device : Device,
dtype : int,
non_blocking : bool=False,
copy : bool=False,
memory_format : Optional[int]) -> Tensor
Tensor.to(dtype : int,
non_blocking : bool=False,
copy : bool=False,
memory_format : Optional[int]) -> Tensor
Tensor.to(other : Tensor,
non_blocking : bool=False,
copy : bool=False,
memory_format : Optional[int]) -> Tensor
Tensor.to(dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool],
non_blocking : bool=False,
copy : bool=False,
memory_format : Optional[int]) -> Tensor
Tensor.to(device : Optional[Device],
dtype : Optional[int],
non_blocking : bool=False,
copy : bool=False) -> Tensor
Tensor.to(dtype : Optional[int],
non_blocking : bool=False,
copy : bool=False) -> Tensor
Tensor.to(non_blocking : bool=False,
copy : bool=False) -> Tensor
Tensor.to_dense(dtype : Optional[int]) -> Tensor
Tensor.to_mkldnn(dtype : Optional[int]) -> Tensor
Tensor.to_sparse(sparse_dim : int) -> Tensor
Tensor.to_sparse() -> Tensor
Tensor.topk(k : int,
dim : int=-1,
largest : bool=True,
sorted : bool=True) -> Tuple[Tensor, Tensor]
Tensor.topk(k : int,
dim : int=-1,
largest : bool=True,
sorted : bool=True,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
Tensor.trace() -> Tensor
Tensor.transpose(dim0 : int,
dim1 : int) -> Tensor
Tensor.transpose(dim0 : str,
dim1 : str) -> Tensor
Tensor.transpose_(dim0 : int,
dim1 : int) -> Tensor
Tensor.triangular_solve(A : Tensor,
upper : bool=True,
transpose : bool=False,
unitriangular : bool=False) -> Tuple[Tensor, Tensor]
Tensor.triangular_solve(A : Tensor,
upper : bool=True,
transpose : bool=False,
unitriangular : bool=False,
X : Tensor,
M : Tensor) -> Tuple[Tensor, Tensor]
Tensor.tril(diagonal : int=0) -> Tensor
Tensor.tril(diagonal : int=0,
out : Tensor) -> Tensor
Tensor.tril_(diagonal : int=0) -> Tensor
Tensor.triu(diagonal : int=0) -> Tensor
Tensor.triu(diagonal : int=0,
out : Tensor) -> Tensor
Tensor.triu_(diagonal : int=0) -> Tensor
Tensor.true_divide(other : number) -> Tensor
Tensor.true_divide(other : Tensor) -> Tensor
Tensor.true_divide(other : Tensor,
out : Tensor) -> Tensor
Tensor.true_divide_(other : number) -> Tensor
Tensor.true_divide_(other : Tensor) -> Tensor
Tensor.trunc() -> Tensor
Tensor.trunc(out : Tensor) -> Tensor
Tensor.trunc_() -> Tensor
Tensor.type_as(other : Tensor) -> Tensor
Tensor.unbind(dim : int=0) -> List[Tensor]
Tensor.unbind(dim : str) -> List[Tensor]
Tensor.unflatten(dim : int,
sizes : List[int],
names : Optional[List[str]]) -> Tensor
Tensor.unflatten(dim : str,
sizes : List[int],
names : List[str]) -> Tensor
Tensor.unfold(dimension : int,
size : int,
step : int) -> Tensor
Tensor.uniform_(from : float=0.0,
to : float=1.0,
generator : Optional[Generator]) -> Tensor
Tensor.unique_consecutive(return_inverse : bool=False,
return_counts : bool=False,
dim : Optional[int]) -> Tuple[Tensor, Tensor, Tensor]
Tensor.unsafe_chunk(chunks : int,
dim : int=0) -> List[Tensor]
Tensor.unsafe_split(split_size : int,
dim : int=0) -> List[Tensor]
Tensor.unsafe_split_with_sizes(split_sizes : List[int],
dim : int=0) -> List[Tensor]
Tensor.unsqueeze(dim : int) -> Tensor
Tensor.unsqueeze_(dim : int) -> Tensor
Tensor.values() -> Tensor
Tensor.var(unbiased : bool=True) -> Tensor
Tensor.var(dim : List[int],
unbiased : bool=True,
keepdim : bool=False) -> Tensor
Tensor.var(dim : List[str],
unbiased : bool=True,
keepdim : bool=False) -> Tensor
Tensor.var(dim : List[str],
unbiased : bool=True,
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.var(dim : List[int],
unbiased : bool=True,
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.var(dim : Optional[List[int]],
correction : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.var(dim : Optional[List[int]],
correction : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.var(dim : List[str],
correction : Optional[int],
keepdim : bool=False) -> Tensor
Tensor.var(dim : List[str],
correction : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
Tensor.vdot(other : Tensor) -> Tensor
Tensor.vdot(other : Tensor,
out : Tensor) -> Tensor
Tensor.view(size : List[int]) -> Tensor
Tensor.view(dtype : int) -> Tensor
Tensor.view_as(other : Tensor) -> Tensor
Tensor.vsplit(sections : int) -> List[Tensor]
Tensor.vsplit(indices : List[int]) -> List[Tensor]
Tensor.xlogy(other : Tensor) -> Tensor
Tensor.xlogy(other : Tensor,
out : Tensor) -> Tensor
Tensor.xlogy(other : number) -> Tensor
Tensor.xlogy(other : number,
out : Tensor) -> Tensor
Tensor.xlogy_(other : Tensor) -> Tensor
Tensor.xlogy_(other : number) -> Tensor
Tensor.zero_() -> Tensor
Supported PyTorch Functions¶
torch.nn.functional.adaptive_avg_pool2d(input : Tensor,
output_size : List[int]) -> Tensor
torch.nn.functional.adaptive_avg_pool3d(input : Tensor,
output_size : List[int]) -> Tensor
torch.nn.functional.adaptive_max_pool1d_with_indices(input : Tensor,
output_size : List[int],
return_indices : bool=False) -> Tuple[Tensor, Tensor]
torch.nn.functional.adaptive_max_pool2d_with_indices(input : Tensor,
output_size : List[int],
return_indices : bool=False) -> Tuple[Tensor, Tensor]
torch.nn.functional.adaptive_max_pool3d_with_indices(input : Tensor,
output_size : List[int],
return_indices : bool=False) -> Tuple[Tensor, Tensor]
torch.nn.functional.affine_grid(theta : Tensor,
size : List[int],
align_corners : Optional[bool]) -> Tensor
torch.nn.functional.alpha_dropout(input : Tensor,
p : float=0.5,
training : bool=False,
inplace : bool=False) -> Tensor
torch.nn.functional.assert_int_or_pair(arg : List[int],
arg_name : str,
message : str) -> NoneType
torch.nn.functional.batch_norm(input : Tensor,
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
weight : Optional[Tensor],
bias : Optional[Tensor],
training : bool=False,
momentum : float=0.1,
eps : float=1e-05) -> Tensor
torch.nn.functional.bilinear(input1 : Tensor,
input2 : Tensor,
weight : Tensor,
bias : Optional[Tensor]) -> Tensor
torch.nn.functional.binary_cross_entropy(input : Tensor,
target : Tensor,
weight : Optional[Tensor],
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.binary_cross_entropy_with_logits(input : Tensor,
target : Tensor,
weight : Optional[Tensor],
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean,
pos_weight : Optional[Tensor]) -> Tensor
torch.nn.functional.celu(input : Tensor,
alpha : float=1.0,
inplace : bool=False) -> Tensor
torch.nn.functional.cosine_embedding_loss(input1 : Tensor,
input2 : Tensor,
target : Tensor,
margin : float=0.0,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.cross_entropy(input : Tensor,
target : Tensor,
weight : Optional[Tensor],
size_average : Optional[bool],
ignore_index : int=-100,
reduce : Optional[bool],
reduction : str=mean,
label_smoothing : float=0.0) -> Tensor
torch.nn.functional.ctc_loss(log_probs : Tensor,
targets : Tensor,
input_lengths : Tensor,
target_lengths : Tensor,
blank : int=0,
reduction : str=mean,
zero_infinity : bool=False) -> Tensor
torch.nn.functional.dropout(input : Tensor,
p : float=0.5,
training : bool=True,
inplace : bool=False) -> Tensor
torch.nn.functional.dropout2d(input : Tensor,
p : float=0.5,
training : bool=True,
inplace : bool=False) -> Tensor
torch.nn.functional.dropout3d(input : Tensor,
p : float=0.5,
training : bool=True,
inplace : bool=False) -> Tensor
torch.nn.functional.elu(input : Tensor,
alpha : float=1.0,
inplace : bool=False) -> Tensor
torch.nn.functional.embedding(input : Tensor,
weight : Tensor,
padding_idx : Optional[int],
max_norm : Optional[float],
norm_type : float=2.0,
scale_grad_by_freq : bool=False,
sparse : bool=False) -> Tensor
torch.nn.functional.embedding_bag(input : Tensor,
weight : Tensor,
offsets : Optional[Tensor],
max_norm : Optional[float],
norm_type : float=2.0,
scale_grad_by_freq : bool=False,
mode : str=mean,
sparse : bool=False,
per_sample_weights : Optional[Tensor],
include_last_offset : bool=False,
padding_idx : Optional[int]) -> Tensor
torch.nn.functional.feature_alpha_dropout(input : Tensor,
p : float=0.5,
training : bool=False,
inplace : bool=False) -> Tensor
torch.nn.functional.fold(input : Tensor,
output_size : List[int],
kernel_size : List[int],
dilation : List[int]=1,
padding : List[int]=0,
stride : List[int]=1) -> Tensor
torch.nn.functional.fractional_max_pool2d_with_indices(input : Tensor,
kernel_size : List[int],
output_size : Optional[List[int]],
output_ratio : Optional[List[float]],
return_indices : bool=False,
_random_samples : Optional[Tensor]) -> Tuple[Tensor, Tensor]
torch.nn.functional.fractional_max_pool3d_with_indices(input : Tensor,
kernel_size : List[int],
output_size : Optional[List[int]],
output_ratio : Optional[List[float]],
return_indices : bool=False,
_random_samples : Optional[Tensor]) -> Tuple[Tensor, Tensor]
torch.nn.functional.gaussian_nll_loss(input : Tensor,
target : Tensor,
var : Tensor,
full : bool=False,
eps : float=1e-06,
reduction : str=mean) -> Tensor
torch.nn.functional.gelu(input : Tensor) -> Tensor
torch.nn.functional.glu(input : Tensor,
dim : int=-1) -> Tensor
torch.nn.functional.grid_sample(input : Tensor,
grid : Tensor,
mode : str=bilinear,
padding_mode : str=zeros,
align_corners : Optional[bool]) -> Tensor
torch.nn.functional.group_norm(input : Tensor,
num_groups : int,
weight : Optional[Tensor],
bias : Optional[Tensor],
eps : float=1e-05) -> Tensor
torch.nn.functional.gumbel_softmax(logits : Tensor,
tau : float=1.0,
hard : bool=False,
eps : float=1e-10,
dim : int=-1) -> Tensor
torch.nn.functional.hardshrink(input : Tensor,
lambd : float=0.5) -> Tensor
torch.nn.functional.hardsigmoid(input : Tensor,
inplace : bool=False) -> Tensor
torch.nn.functional.hardswish(input : Tensor,
inplace : bool=False) -> Tensor
torch.nn.functional.hardtanh(input : Tensor,
min_val : float=-1.0,
max_val : float=1.0,
inplace : bool=False) -> Tensor
torch.nn.functional.hinge_embedding_loss(input : Tensor,
target : Tensor,
margin : float=1.0,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.huber_loss(input : Tensor,
target : Tensor,
reduction : str=mean,
delta : float=1.0) -> Tensor
torch.nn.functional.instance_norm(input : Tensor,
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
weight : Optional[Tensor],
bias : Optional[Tensor],
use_input_stats : bool=True,
momentum : float=0.1,
eps : float=1e-05) -> Tensor
torch.nn.functional.kl_div(input : Tensor,
target : Tensor,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean,
log_target : bool=False) -> Tensor
torch.nn.functional.l1_loss(input : Tensor,
target : Tensor,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.layer_norm(input : Tensor,
normalized_shape : List[int],
weight : Optional[Tensor],
bias : Optional[Tensor],
eps : float=1e-05) -> Tensor
torch.nn.functional.leaky_relu(input : Tensor,
negative_slope : float=0.01,
inplace : bool=False) -> Tensor
torch.nn.functional.linear(input : Tensor,
weight : Tensor,
bias : Optional[Tensor]) -> Tensor
torch.nn.functional.local_response_norm(input : Tensor,
size : int,
alpha : float=0.0001,
beta : float=0.75,
k : float=1.0) -> Tensor
torch.nn.functional.log_softmax(input : Tensor,
dim : Optional[int],
_stacklevel : int=3,
dtype : Optional[int]) -> Tensor
torch.nn.functional.lp_pool1d(input : Tensor,
norm_type : float,
kernel_size : int,
stride : Optional[List[int]],
ceil_mode : bool=False) -> Tensor
torch.nn.functional.lp_pool2d(input : Tensor,
norm_type : float,
kernel_size : int,
stride : Optional[List[int]],
ceil_mode : bool=False) -> Tensor
torch.nn.functional.margin_ranking_loss(input1 : Tensor,
input2 : Tensor,
target : Tensor,
margin : float=0.0,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.max_pool1d_with_indices(input : Tensor,
kernel_size : List[int],
stride : Optional[List[int]],
padding : List[int]=0,
dilation : List[int]=1,
ceil_mode : bool=False,
return_indices : bool=False) -> Tuple[Tensor, Tensor]
torch.nn.functional.max_pool2d_with_indices(input : Tensor,
kernel_size : List[int],
stride : Optional[List[int]],
padding : List[int]=0,
dilation : List[int]=1,
ceil_mode : bool=False,
return_indices : bool=False) -> Tuple[Tensor, Tensor]
torch.nn.functional.max_pool3d_with_indices(input : Tensor,
kernel_size : List[int],
stride : Optional[List[int]],
padding : List[int]=0,
dilation : List[int]=1,
ceil_mode : bool=False,
return_indices : bool=False) -> Tuple[Tensor, Tensor]
torch.nn.functional.max_unpool1d(input : Tensor,
indices : Tensor,
kernel_size : List[int],
stride : Optional[List[int]],
padding : List[int]=0,
output_size : Optional[List[int]]) -> Tensor
torch.nn.functional.max_unpool2d(input : Tensor,
indices : Tensor,
kernel_size : List[int],
stride : Optional[List[int]],
padding : List[int]=0,
output_size : Optional[List[int]]) -> Tensor
torch.nn.functional.max_unpool3d(input : Tensor,
indices : Tensor,
kernel_size : List[int],
stride : Optional[List[int]],
padding : List[int]=0,
output_size : Optional[List[int]]) -> Tensor
torch.nn.functional.mish(input : Tensor,
inplace : bool=False) -> Tensor
torch.nn.functional.mse_loss(input : Tensor,
target : Tensor,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.multi_head_attention_forward(query : Tensor,
key : Tensor,
value : Tensor,
embed_dim_to_check : int,
num_heads : int,
in_proj_weight : Tensor,
in_proj_bias : Optional[Tensor],
bias_k : Optional[Tensor],
bias_v : Optional[Tensor],
add_zero_attn : bool,
dropout_p : float,
out_proj_weight : Tensor,
out_proj_bias : Optional[Tensor],
training : bool=True,
key_padding_mask : Optional[Tensor],
need_weights : bool=True,
attn_mask : Optional[Tensor],
use_separate_proj_weight : bool=False,
q_proj_weight : Optional[Tensor],
k_proj_weight : Optional[Tensor],
v_proj_weight : Optional[Tensor],
static_k : Optional[Tensor],
static_v : Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]]
torch.nn.functional.multi_margin_loss(input : Tensor,
target : Tensor,
p : int=1,
margin : float=1.0,
weight : Optional[Tensor],
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.multilabel_margin_loss(input : Tensor,
target : Tensor,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.multilabel_soft_margin_loss(input : Tensor,
target : Tensor,
weight : Optional[Tensor],
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.nll_loss(input : Tensor,
target : Tensor,
weight : Optional[Tensor],
size_average : Optional[bool],
ignore_index : int=-100,
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.normalize(input : Tensor,
p : float=2.0,
dim : int=1,
eps : float=1e-12,
out : Optional[Tensor]) -> Tensor
torch.nn.functional.pad(input : Tensor,
pad : List[int],
mode : str=constant,
value : float=0.0) -> Tensor
torch.nn.functional.pairwise_distance(x1 : Tensor,
x2 : Tensor,
p : float=2.0,
eps : float=1e-06,
keepdim : bool=False) -> Tensor
torch.nn.functional.poisson_nll_loss(input : Tensor,
target : Tensor,
log_input : bool=True,
full : bool=False,
size_average : Optional[bool],
eps : float=1e-08,
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.prelu(input : Tensor,
weight : Tensor) -> Tensor
torch.nn.functional.relu(input : Tensor,
inplace : bool=False) -> Tensor
torch.nn.functional.relu6(input : Tensor,
inplace : bool=False) -> Tensor
torch.nn.functional.rrelu(input : Tensor,
lower : float=0.125,
upper : float=0.3333333333333333,
training : bool=False,
inplace : bool=False) -> Tensor
torch.nn.functional.selu(input : Tensor,
inplace : bool=False) -> Tensor
torch.nn.functional.sigmoid(input : Tensor) -> Tensor
torch.nn.functional.silu(input : Tensor,
inplace : bool=False) -> Tensor
torch.nn.functional.smooth_l1_loss(input : Tensor,
target : Tensor,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean,
beta : float=1.0) -> Tensor
torch.nn.functional.soft_margin_loss(input : Tensor,
target : Tensor,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.softmax(input : Tensor,
dim : Optional[int],
_stacklevel : int=3,
dtype : Optional[int]) -> Tensor
torch.nn.functional.softmin(input : Tensor,
dim : Optional[int],
_stacklevel : int=3,
dtype : Optional[int]) -> Tensor
torch.nn.functional.softsign(input : Tensor) -> Tensor
torch.nn.functional.tanh(input : Tensor) -> Tensor
torch.nn.functional.tanhshrink(input : Tensor) -> Tensor
torch.nn.functional.threshold(input : Tensor,
threshold : float,
value : float,
inplace : bool=False) -> Tensor
torch.nn.functional.triplet_margin_loss(anchor : Tensor,
positive : Tensor,
negative : Tensor,
margin : float=1.0,
p : float=2.0,
eps : float=1e-06,
swap : bool=False,
size_average : Optional[bool],
reduce : Optional[bool],
reduction : str=mean) -> Tensor
torch.nn.functional.unfold(input : Tensor,
kernel_size : List[int],
dilation : List[int]=1,
padding : List[int]=0,
stride : List[int]=1) -> Tensor
torch.Size(sizes : List[int]) -> List[int]
torch.abs(self : Tensor) -> Tensor
torch.abs(self : Tensor,
out : Tensor) -> Tensor
torch.abs_(self : Tensor) -> Tensor
torch.absolute(self : Tensor) -> Tensor
torch.absolute(self : Tensor,
out : Tensor) -> Tensor
torch.acos(self : Tensor) -> Tensor
torch.acos(self : Tensor,
out : Tensor) -> Tensor
torch.acos(a : int) -> float
torch.acos(a : float) -> float
torch.acos(a : complex) -> complex
torch.acos(a : number) -> number
torch.acos_(self : Tensor) -> Tensor
torch.acosh(self : Tensor) -> Tensor
torch.acosh(self : Tensor,
out : Tensor) -> Tensor
torch.acosh(a : int) -> float
torch.acosh(a : float) -> float
torch.acosh(a : complex) -> complex
torch.acosh(a : number) -> number
torch.acosh_(self : Tensor) -> Tensor
torch.adaptive_avg_pool1d(self : Tensor,
output_size : List[int]) -> Tensor
torch.adaptive_max_pool1d(self : Tensor,
output_size : List[int]) -> Tuple[Tensor, Tensor]
torch.add(self : Tensor,
other : Tensor,
alpha : number=1) -> Tensor
torch.add(self : Tensor,
other : number,
alpha : number=1) -> Tensor
torch.add(self : Tensor,
other : Tensor,
alpha : number=1,
out : Tensor) -> Tensor
torch.add(a : List[t],
b : List[t]) -> List[t]
torch.add(a : str,
b : str) -> str
torch.add(a : int,
b : int) -> int
torch.add(a : complex,
b : complex) -> complex
torch.add(a : float,
b : float) -> float
torch.add(a : int,
b : complex) -> complex
torch.add(a : complex,
b : int) -> complex
torch.add(a : float,
b : complex) -> complex
torch.add(a : complex,
b : float) -> complex
torch.add(a : int,
b : float) -> float
torch.add(a : float,
b : int) -> float
torch.add(a : number,
b : number) -> number
torch.addbmm(self : Tensor,
batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
torch.addbmm(self : Tensor,
batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
torch.addcdiv(self : Tensor,
tensor1 : Tensor,
tensor2 : Tensor,
value : number=1) -> Tensor
torch.addcdiv(self : Tensor,
tensor1 : Tensor,
tensor2 : Tensor,
value : number=1,
out : Tensor) -> Tensor
torch.addcmul(self : Tensor,
tensor1 : Tensor,
tensor2 : Tensor,
value : number=1) -> Tensor
torch.addcmul(self : Tensor,
tensor1 : Tensor,
tensor2 : Tensor,
value : number=1,
out : Tensor) -> Tensor
torch.addmm(self : Tensor,
mat1 : Tensor,
mat2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
torch.addmm(self : Tensor,
mat1 : Tensor,
mat2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
torch.addmv(self : Tensor,
mat : Tensor,
vec : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
torch.addmv(self : Tensor,
mat : Tensor,
vec : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
torch.addmv_(self : Tensor,
mat : Tensor,
vec : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
torch.addr(self : Tensor,
vec1 : Tensor,
vec2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
torch.addr(self : Tensor,
vec1 : Tensor,
vec2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
torch.affine_grid_generator(theta : Tensor,
size : List[int],
align_corners : bool) -> Tensor
torch.align_tensors(tensors : List[Tensor]) -> List[Tensor]
torch.all(self : Tensor) -> Tensor
torch.all(self : Tensor,
dim : int,
keepdim : bool=False) -> Tensor
torch.all(self : Tensor,
dim : int,
keepdim : bool=False,
out : Tensor) -> Tensor
torch.all(self : Tensor,
out : Tensor) -> Tensor
torch.all(self : Tensor,
dim : str,
keepdim : bool=False) -> Tensor
torch.all(self : Tensor,
dim : str,
keepdim : bool=False,
out : Tensor) -> Tensor
torch.all(self : List[int]) -> bool
torch.all(self : List[float]) -> bool
torch.all(self : List[bool]) -> bool
torch.allclose(self : Tensor,
other : Tensor,
rtol : float=1e-05,
atol : float=1e-08,
equal_nan : bool=False) -> bool
torch.alpha_dropout(input : Tensor,
p : float,
train : bool) -> Tensor
torch.alpha_dropout_(self : Tensor,
p : float,
train : bool) -> Tensor
torch.amax(self : Tensor,
dim : List[int]=[],
keepdim : bool=False,
out : Tensor) -> Tensor
torch.amax(self : Tensor,
dim : List[int]=[],
keepdim : bool=False) -> Tensor
torch.amin(self : Tensor,
dim : List[int]=[],
keepdim : bool=False,
out : Tensor) -> Tensor
torch.amin(self : Tensor,
dim : List[int]=[],
keepdim : bool=False) -> Tensor
torch.aminmax(self : Tensor,
dim : Optional[int],
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.aminmax(self : Tensor,
dim : Optional[int],
keepdim : bool=False,
min : Tensor,
max : Tensor) -> Tuple[Tensor, Tensor]
torch.angle(self : Tensor) -> Tensor
torch.angle(self : Tensor,
out : Tensor) -> Tensor
torch.angle(a : int) -> float
torch.angle(a : float) -> float
torch.angle(a : complex) -> float
torch.angle(a : number) -> number
torch.any(self : Tensor) -> Tensor
torch.any(self : Tensor,
dim : int,
keepdim : bool=False) -> Tensor
torch.any(self : Tensor,
dim : int,
keepdim : bool=False,
out : Tensor) -> Tensor
torch.any(self : Tensor,
out : Tensor) -> Tensor
torch.any(self : Tensor,
dim : str,
keepdim : bool=False) -> Tensor
torch.any(self : Tensor,
dim : str,
keepdim : bool=False,
out : Tensor) -> Tensor
torch.any(self : List[str]) -> bool
torch.any(self : List[int]) -> bool
torch.any(self : List[float]) -> bool
torch.any(self : List[bool]) -> bool
torch.arange(end : number,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.arange(start : number,
end : number,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.arange(start : number,
end : number,
step : number,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.arange(start : number,
end : number,
step : number=1,
out : Tensor) -> Tensor
torch.arange(end : number,
out : Tensor) -> Tensor
torch.arccos(self : Tensor) -> Tensor
torch.arccos(self : Tensor,
out : Tensor) -> Tensor
torch.arccos_(self : Tensor) -> Tensor
torch.arccosh(self : Tensor) -> Tensor
torch.arccosh(self : Tensor,
out : Tensor) -> Tensor
torch.arccosh_(self : Tensor) -> Tensor
torch.arcsin(self : Tensor) -> Tensor
torch.arcsin(self : Tensor,
out : Tensor) -> Tensor
torch.arcsin_(self : Tensor) -> Tensor
torch.arcsinh(self : Tensor) -> Tensor
torch.arcsinh(self : Tensor,
out : Tensor) -> Tensor
torch.arcsinh_(self : Tensor) -> Tensor
torch.arctan(self : Tensor) -> Tensor
torch.arctan(self : Tensor,
out : Tensor) -> Tensor
torch.arctan_(self : Tensor) -> Tensor
torch.arctanh(self : Tensor) -> Tensor
torch.arctanh(self : Tensor,
out : Tensor) -> Tensor
torch.arctanh_(self : Tensor) -> Tensor
torch.argmax(self : Tensor,
dim : Optional[int],
keepdim : bool=False) -> Tensor
torch.argmax(self : Tensor,
dim : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
torch.argmin(self : Tensor,
dim : Optional[int],
keepdim : bool=False) -> Tensor
torch.argmin(self : Tensor,
dim : Optional[int],
keepdim : bool=False,
out : Tensor) -> Tensor
torch.argsort(self : Tensor,
dim : int=-1,
descending : bool=False) -> Tensor
torch.argsort(self : Tensor,
dim : str,
descending : bool=False) -> Tensor
torch.as_strided(self : Tensor,
size : List[int],
stride : List[int],
storage_offset : Optional[int]) -> Tensor
torch.as_strided_(self : Tensor,
size : List[int],
stride : List[int],
storage_offset : Optional[int]) -> Tensor
torch.as_tensor(t : float,
dtype : Optional[int],
device : Optional[Device]) -> Tensor
torch.as_tensor(t : int,
dtype : Optional[int],
device : Optional[Device]) -> Tensor
torch.as_tensor(t : bool,
dtype : Optional[int],
device : Optional[Device]) -> Tensor
torch.as_tensor(t : complex,
dtype : Optional[int],
device : Optional[Device]) -> Tensor
torch.as_tensor(data : Tensor,
dtype : Optional[int],
device : Optional[Device]) -> Tensor
torch.as_tensor(data : List[t],
dtype : Optional[int],
device : Optional[Device]) -> Tensor
torch.asin(self : Tensor) -> Tensor
torch.asin(self : Tensor,
out : Tensor) -> Tensor
torch.asin(a : int) -> float
torch.asin(a : float) -> float
torch.asin(a : complex) -> complex
torch.asin(a : number) -> number
torch.asin_(self : Tensor) -> Tensor
torch.asinh(self : Tensor) -> Tensor
torch.asinh(self : Tensor,
out : Tensor) -> Tensor
torch.asinh(a : int) -> float
torch.asinh(a : float) -> float
torch.asinh(a : complex) -> complex
torch.asinh(a : number) -> number
torch.asinh_(self : Tensor) -> Tensor
torch.atan(self : Tensor) -> Tensor
torch.atan(self : Tensor,
out : Tensor) -> Tensor
torch.atan(a : int) -> float
torch.atan(a : float) -> float
torch.atan(a : complex) -> complex
torch.atan(a : number) -> number
torch.atan2(self : Tensor,
other : Tensor) -> Tensor
torch.atan2(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.atan2(a : int,
b : int) -> float
torch.atan2(a : float,
b : float) -> float
torch.atan2(a : int,
b : float) -> float
torch.atan2(a : float,
b : int) -> float
torch.atan2(a : number,
b : number) -> float
torch.atan_(self : Tensor) -> Tensor
torch.atanh(self : Tensor) -> Tensor
torch.atanh(self : Tensor,
out : Tensor) -> Tensor
torch.atanh(a : int) -> float
torch.atanh(a : float) -> float
torch.atanh(a : complex) -> complex
torch.atanh(a : number) -> number
torch.atanh_(self : Tensor) -> Tensor
torch.atleast_1d(self : Tensor) -> Tensor
torch.atleast_1d(tensors : List[Tensor]) -> List[Tensor]
torch.atleast_2d(self : Tensor) -> Tensor
torch.atleast_2d(tensors : List[Tensor]) -> List[Tensor]
torch.atleast_3d(self : Tensor) -> Tensor
torch.atleast_3d(tensors : List[Tensor]) -> List[Tensor]
torch.avg_pool1d(self : Tensor,
kernel_size : List[int],
stride : List[int]=[],
padding : List[int]=[0],
ceil_mode : bool=False,
count_include_pad : bool=True) -> Tensor
torch.baddbmm(self : Tensor,
batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1) -> Tensor
torch.baddbmm(self : Tensor,
batch1 : Tensor,
batch2 : Tensor,
beta : number=1,
alpha : number=1,
out : Tensor) -> Tensor
torch.bartlett_window(window_length : int,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.bartlett_window(window_length : int,
periodic : bool,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.batch_norm(input : Tensor,
weight : Optional[Tensor],
bias : Optional[Tensor],
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
training : bool,
momentum : float,
eps : float,
cudnn_enabled : bool) -> Tensor
torch.batch_norm_backward_elemt(grad_out : Tensor,
input : Tensor,
mean : Tensor,
invstd : Tensor,
weight : Optional[Tensor],
mean_dy : Tensor,
mean_dy_xmu : Tensor,
count : Tensor) -> Tensor
torch.batch_norm_backward_reduce(grad_out : Tensor,
input : Tensor,
mean : Tensor,
invstd : Tensor,
weight : Optional[Tensor],
input_g : bool,
weight_g : bool,
bias_g : bool) -> Tuple[Tensor, Tensor, Tensor, Tensor]
torch.batch_norm_elemt(input : Tensor,
weight : Optional[Tensor],
bias : Optional[Tensor],
mean : Tensor,
invstd : Tensor,
eps : float) -> Tensor
torch.batch_norm_elemt(input : Tensor,
weight : Optional[Tensor],
bias : Optional[Tensor],
mean : Tensor,
invstd : Tensor,
eps : float,
out : Tensor) -> Tensor
torch.batch_norm_gather_stats(input : Tensor,
mean : Tensor,
invstd : Tensor,
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
momentum : float,
eps : float,
count : int) -> Tuple[Tensor, Tensor]
torch.batch_norm_gather_stats_with_counts(input : Tensor,
mean : Tensor,
invstd : Tensor,
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
momentum : float,
eps : float,
counts : Tensor) -> Tuple[Tensor, Tensor]
torch.batch_norm_stats(input : Tensor,
eps : float) -> Tuple[Tensor, Tensor]
torch.batch_norm_update_stats(input : Tensor,
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
momentum : float) -> Tuple[Tensor, Tensor]
torch.bernoulli(self : Tensor,
generator : Optional[Generator]) -> Tensor
torch.bernoulli(self : Tensor,
generator : Optional[Generator],
out : Tensor) -> Tensor
torch.bernoulli(self : Tensor,
p : float,
generator : Optional[Generator]) -> Tensor
torch.bilinear(input1 : Tensor,
input2 : Tensor,
weight : Tensor,
bias : Optional[Tensor]) -> Tensor
torch.binary_cross_entropy_with_logits(self : Tensor,
target : Tensor,
weight : Optional[Tensor],
pos_weight : Optional[Tensor],
reduction : int=1) -> Tensor
torch.bincount(self : Tensor,
weights : Optional[Tensor],
minlength : int=0) -> Tensor
torch.binomial(count : Tensor,
prob : Tensor,
generator : Optional[Generator]) -> Tensor
torch.bitwise_and(self : Tensor,
other : Tensor) -> Tensor
torch.bitwise_and(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.bitwise_and(self : Tensor,
other : number) -> Tensor
torch.bitwise_and(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.bitwise_left_shift(self : Tensor,
other : Tensor) -> Tensor
torch.bitwise_left_shift(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.bitwise_left_shift(self : Tensor,
other : number) -> Tensor
torch.bitwise_left_shift(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.bitwise_left_shift(self : number,
other : Tensor) -> Tensor
torch.bitwise_not(self : Tensor) -> Tensor
torch.bitwise_not(self : Tensor,
out : Tensor) -> Tensor
torch.bitwise_or(self : Tensor,
other : Tensor) -> Tensor
torch.bitwise_or(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.bitwise_or(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.bitwise_or(self : Tensor,
other : number) -> Tensor
torch.bitwise_right_shift(self : Tensor,
other : Tensor) -> Tensor
torch.bitwise_right_shift(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.bitwise_right_shift(self : Tensor,
other : number) -> Tensor
torch.bitwise_right_shift(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.bitwise_right_shift(self : number,
other : Tensor) -> Tensor
torch.bitwise_xor(self : Tensor,
other : Tensor) -> Tensor
torch.bitwise_xor(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.bitwise_xor(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.bitwise_xor(self : Tensor,
other : number) -> Tensor
torch.blackman_window(window_length : int,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.blackman_window(window_length : int,
periodic : bool,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.block_diag(tensors : List[Tensor]) -> Tensor
torch.bmm(self : Tensor,
mat2 : Tensor) -> Tensor
torch.bmm(self : Tensor,
mat2 : Tensor,
out : Tensor) -> Tensor
torch.broadcast_tensors(tensors : List[Tensor]) -> List[Tensor]
torch.broadcast_to(self : Tensor,
size : List[int]) -> Tensor
torch.bucketize(self : Tensor,
boundaries : Tensor,
out_int32 : bool=False,
right : bool=False) -> Tensor
torch.bucketize(self : Tensor,
boundaries : Tensor,
out_int32 : bool=False,
right : bool=False,
out : Tensor) -> Tensor
torch.bucketize(self : number,
boundaries : Tensor,
out_int32 : bool=False,
right : bool=False) -> Tensor
torch.can_cast(from : int,
to : int) -> bool
torch.candidate(self : Future[t]) -> t
torch.cartesian_prod(tensors : List[Tensor]) -> Tensor
torch.cat(tensors : List[Tensor],
dim : int=0) -> Tensor
torch.cat(tensors : List[Tensor],
dim : str) -> Tensor
torch.cat(tensors : List[Tensor],
dim : str,
out : Tensor) -> Tensor
torch.cat(tensors : List[Tensor],
dim : int=0,
out : Tensor) -> Tensor
torch.ceil(self : Tensor) -> Tensor
torch.ceil(self : Tensor,
out : Tensor) -> Tensor
torch.ceil(a : int) -> int
torch.ceil(a : float) -> int
torch.ceil(a : number) -> number
torch.ceil_(self : Tensor) -> Tensor
torch.celu(self : Tensor,
alpha : number=1.0) -> Tensor
torch.celu_(self : Tensor,
alpha : number=1.0) -> Tensor
torch.chain_matmul(matrices : List[Tensor]) -> Tensor
torch.chain_matmul(matrices : List[Tensor],
out : Tensor) -> Tensor
torch.channel_shuffle(self : Tensor,
groups : int) -> Tensor
torch.cholesky(self : Tensor,
upper : bool=False) -> Tensor
torch.cholesky(self : Tensor,
upper : bool=False,
out : Tensor) -> Tensor
torch.cholesky_inverse(self : Tensor,
upper : bool=False) -> Tensor
torch.cholesky_inverse(self : Tensor,
upper : bool=False,
out : Tensor) -> Tensor
torch.cholesky_solve(self : Tensor,
input2 : Tensor,
upper : bool=False) -> Tensor
torch.cholesky_solve(self : Tensor,
input2 : Tensor,
upper : bool=False,
out : Tensor) -> Tensor
torch.choose_qparams_optimized(input : Tensor,
numel : int,
n_bins : int,
ratio : float,
bit_width : int) -> Tuple[Tensor, Tensor]
torch.chunk(self : Tensor,
chunks : int,
dim : int=0) -> List[Tensor]
torch.clamp(self : Tensor,
min : Optional[number],
max : Optional[number]) -> Tensor
torch.clamp(self : Tensor,
min : Optional[Tensor],
max : Optional[Tensor]) -> Tensor
torch.clamp(self : Tensor,
min : Optional[number],
max : Optional[number],
out : Tensor) -> Tensor
torch.clamp(self : Tensor,
min : Optional[Tensor],
max : Optional[Tensor],
out : Tensor) -> Tensor
torch.clamp_(self : Tensor,
min : Optional[number],
max : Optional[number]) -> Tensor
torch.clamp_(self : Tensor,
min : Optional[Tensor],
max : Optional[Tensor]) -> Tensor
torch.clamp_max(self : Tensor,
max : number) -> Tensor
torch.clamp_max(self : Tensor,
max : Tensor) -> Tensor
torch.clamp_max(self : Tensor,
max : number,
out : Tensor) -> Tensor
torch.clamp_max(self : Tensor,
max : Tensor,
out : Tensor) -> Tensor
torch.clamp_max_(self : Tensor,
max : number) -> Tensor
torch.clamp_max_(self : Tensor,
max : Tensor) -> Tensor
torch.clamp_min(self : Tensor,
min : number) -> Tensor
torch.clamp_min(self : Tensor,
min : Tensor) -> Tensor
torch.clamp_min(self : Tensor,
min : number,
out : Tensor) -> Tensor
torch.clamp_min(self : Tensor,
min : Tensor,
out : Tensor) -> Tensor
torch.clamp_min_(self : Tensor,
min : number) -> Tensor
torch.clamp_min_(self : Tensor,
min : Tensor) -> Tensor
torch.clip(self : Tensor,
min : Optional[number],
max : Optional[number]) -> Tensor
torch.clip(self : Tensor,
min : Optional[number],
max : Optional[number],
out : Tensor) -> Tensor
torch.clip(self : Tensor,
min : Optional[Tensor],
max : Optional[Tensor]) -> Tensor
torch.clip(self : Tensor,
min : Optional[Tensor],
max : Optional[Tensor],
out : Tensor) -> Tensor
torch.clip_(self : Tensor,
min : Optional[number],
max : Optional[number]) -> Tensor
torch.clip_(self : Tensor,
min : Optional[Tensor],
max : Optional[Tensor]) -> Tensor
torch.clone(self : Tensor,
memory_format : Optional[int]) -> Tensor
torch.column_stack(tensors : List[Tensor]) -> Tensor
torch.column_stack(tensors : List[Tensor],
out : Tensor) -> Tensor
torch.combinations(self : Tensor,
r : int=2,
with_replacement : bool=False) -> Tensor
torch.complex(real : Tensor,
imag : Tensor,
out : Tensor) -> Tensor
torch.complex(real : Tensor,
imag : Tensor) -> Tensor
torch.concat(tensors : List[Tensor],
dim : int=0) -> Tensor
torch.concat(tensors : List[Tensor],
dim : int=0,
out : Tensor) -> Tensor
torch.concat(tensors : List[Tensor],
dim : str) -> Tensor
torch.concat(tensors : List[Tensor],
dim : str,
out : Tensor) -> Tensor
torch.conj(self : Tensor) -> Tensor
torch.conj_physical(self : Tensor) -> Tensor
torch.conj_physical(self : Tensor,
out : Tensor) -> Tensor
torch.conj_physical_(self : Tensor) -> Tensor
torch.constant_pad_nd(self : Tensor,
pad : List[int],
value : number=0) -> Tensor
torch.conv1d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1],
padding : List[int]=[0],
dilation : List[int]=[1],
groups : int=1) -> Tensor
torch.conv1d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1],
padding : str=valid,
dilation : List[int]=[1],
groups : int=1) -> Tensor
torch.conv2d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1, 1],
padding : List[int]=[0, 0],
dilation : List[int]=[1, 1],
groups : int=1) -> Tensor
torch.conv2d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1, 1],
padding : str=valid,
dilation : List[int]=[1, 1],
groups : int=1) -> Tensor
torch.conv3d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1, 1, 1],
padding : List[int]=[0, 0, 0],
dilation : List[int]=[1, 1, 1],
groups : int=1) -> Tensor
torch.conv3d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1, 1, 1],
padding : str=valid,
dilation : List[int]=[1, 1, 1],
groups : int=1) -> Tensor
torch.conv_tbc(self : Tensor,
weight : Tensor,
bias : Tensor,
pad : int=0) -> Tensor
torch.conv_transpose1d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1],
padding : List[int]=[0],
output_padding : List[int]=[0],
groups : int=1,
dilation : List[int]=[1]) -> Tensor
torch.conv_transpose2d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1, 1],
padding : List[int]=[0, 0],
output_padding : List[int]=[0, 0],
groups : int=1,
dilation : List[int]=[1, 1]) -> Tensor
torch.conv_transpose3d(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int]=[1, 1, 1],
padding : List[int]=[0, 0, 0],
output_padding : List[int]=[0, 0, 0],
groups : int=1,
dilation : List[int]=[1, 1, 1]) -> Tensor
torch.convolution(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int],
padding : List[int],
dilation : List[int],
transposed : bool,
output_padding : List[int],
groups : int) -> Tensor
torch.copysign(self : Tensor,
other : Tensor) -> Tensor
torch.copysign(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.copysign(self : Tensor,
other : number) -> Tensor
torch.copysign(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.copysign(a : int,
b : int) -> float
torch.copysign(a : float,
b : float) -> float
torch.copysign(a : int,
b : float) -> float
torch.copysign(a : float,
b : int) -> float
torch.copysign(a : number,
b : number) -> float
torch.corrcoef(self : Tensor) -> Tensor
torch.cos(self : Tensor) -> Tensor
torch.cos(self : Tensor,
out : Tensor) -> Tensor
torch.cos(a : int) -> float
torch.cos(a : float) -> float
torch.cos(a : complex) -> complex
torch.cos(a : number) -> number
torch.cos_(self : Tensor) -> Tensor
torch.cosh(self : Tensor) -> Tensor
torch.cosh(self : Tensor,
out : Tensor) -> Tensor
torch.cosh(a : int) -> float
torch.cosh(a : float) -> float
torch.cosh(a : complex) -> complex
torch.cosh(a : number) -> number
torch.cosh_(self : Tensor) -> Tensor
torch.cosine_embedding_loss(input1 : Tensor,
input2 : Tensor,
target : Tensor,
margin : float=0.0,
reduction : int=1) -> Tensor
torch.cosine_similarity(x1 : Tensor,
x2 : Tensor,
dim : int=1,
eps : float=1e-08) -> Tensor
torch.count_nonzero(self : Tensor,
dim : List[int]) -> Tensor
torch.count_nonzero(self : Tensor,
dim : Optional[int]) -> Tensor
torch.cov(self : Tensor,
correction : int=1,
fweights : Optional[Tensor],
aweights : Optional[Tensor]) -> Tensor
torch.cross(self : Tensor,
other : Tensor,
dim : Optional[int]) -> Tensor
torch.cross(self : Tensor,
other : Tensor,
dim : Optional[int],
out : Tensor) -> Tensor
torch.ctc_loss(log_probs : Tensor,
targets : Tensor,
input_lengths : List[int],
target_lengths : List[int],
blank : int=0,
reduction : int=1,
zero_infinity : bool=False) -> Tensor
torch.ctc_loss(log_probs : Tensor,
targets : Tensor,
input_lengths : Tensor,
target_lengths : Tensor,
blank : int=0,
reduction : int=1,
zero_infinity : bool=False) -> Tensor
torch.cudnn_affine_grid_generator(theta : Tensor,
N : int,
C : int,
H : int,
W : int) -> Tensor
torch.cudnn_batch_norm(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
training : bool,
exponential_average_factor : float,
epsilon : float) -> Tuple[Tensor, Tensor, Tensor, Tensor]
torch.cudnn_convolution(self : Tensor,
weight : Tensor,
bias : Optional[Tensor],
padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool) -> Tensor
torch.cudnn_convolution(self : Tensor,
weight : Tensor,
padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool) -> Tensor
torch.cudnn_convolution(self : Tensor,
weight : Tensor,
padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool,
allow_tf32 : bool) -> Tensor
torch.cudnn_convolution_add_relu(self : Tensor,
weight : Tensor,
z : Tensor,
alpha : Optional[number],
bias : Optional[Tensor],
stride : List[int],
padding : List[int],
dilation : List[int],
groups : int) -> Tensor
torch.cudnn_convolution_relu(self : Tensor,
weight : Tensor,
bias : Optional[Tensor],
stride : List[int],
padding : List[int],
dilation : List[int],
groups : int) -> Tensor
torch.cudnn_convolution_transpose(self : Tensor,
weight : Tensor,
bias : Optional[Tensor],
padding : List[int],
output_padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool) -> Tensor
torch.cudnn_convolution_transpose(self : Tensor,
weight : Tensor,
padding : List[int],
output_padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool) -> Tensor
torch.cudnn_convolution_transpose(self : Tensor,
weight : Tensor,
padding : List[int],
output_padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool,
allow_tf32 : bool) -> Tensor
torch.cudnn_grid_sampler(self : Tensor,
grid : Tensor) -> Tensor
torch.cudnn_is_acceptable(self : Tensor) -> bool
torch.cummax(self : Tensor,
dim : int) -> Tuple[Tensor, Tensor]
torch.cummax(self : Tensor,
dim : str) -> Tuple[Tensor, Tensor]
torch.cummax(self : Tensor,
dim : str,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.cummax(self : Tensor,
dim : int,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.cummin(self : Tensor,
dim : int) -> Tuple[Tensor, Tensor]
torch.cummin(self : Tensor,
dim : str) -> Tuple[Tensor, Tensor]
torch.cummin(self : Tensor,
dim : str,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.cummin(self : Tensor,
dim : int,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.cumprod(self : Tensor,
dim : int,
dtype : Optional[int]) -> Tensor
torch.cumprod(self : Tensor,
dim : str,
dtype : Optional[int]) -> Tensor
torch.cumprod(self : Tensor,
dim : str,
dtype : Optional[int],
out : Tensor) -> Tensor
torch.cumprod(self : Tensor,
dim : int,
dtype : Optional[int],
out : Tensor) -> Tensor
torch.cumsum(self : Tensor,
dim : int,
dtype : Optional[int]) -> Tensor
torch.cumsum(self : Tensor,
dim : str,
dtype : Optional[int]) -> Tensor
torch.cumsum(self : Tensor,
dim : str,
dtype : Optional[int],
out : Tensor) -> Tensor
torch.cumsum(self : Tensor,
dim : int,
dtype : Optional[int],
out : Tensor) -> Tensor
torch.cumulative_trapezoid(y : Tensor,
x : Tensor,
dim : int=-1) -> Tensor
torch.cumulative_trapezoid(y : Tensor,
dx : number=1,
dim : int=-1) -> Tensor
torch.deg2rad(self : Tensor) -> Tensor
torch.deg2rad(self : Tensor,
out : Tensor) -> Tensor
torch.deg2rad_(self : Tensor) -> Tensor
torch.dequantize(self : Tensor) -> Tensor
torch.dequantize(tensors : List[Tensor]) -> List[Tensor]
torch.dequantize(qtensor : Tensor) -> Tensor
torch.dequantize(qtensors : List[Tensor]) -> List[Tensor]
torch.dequantize(tensors : Any) -> Any
torch.det(self : Tensor) -> Tensor
torch.detach(self : Tensor) -> Tensor
torch.detach_(self : Tensor) -> Tensor
torch.device(a : str) -> Device
torch.diag(self : Tensor,
diagonal : int=0) -> Tensor
torch.diag(self : Tensor,
diagonal : int=0,
out : Tensor) -> Tensor
torch.diag_embed(self : Tensor,
offset : int=0,
dim1 : int=-2,
dim2 : int=-1) -> Tensor
torch.diagflat(self : Tensor,
offset : int=0) -> Tensor
torch.diagonal(self : Tensor,
offset : int=0,
dim1 : int=0,
dim2 : int=1) -> Tensor
torch.diagonal(self : Tensor,
outdim : str,
dim1 : str,
dim2 : str,
offset : int=0) -> Tensor
torch.diff(self : Tensor,
n : int=1,
dim : int=-1,
prepend : Optional[Tensor],
append : Optional[Tensor]) -> Tensor
torch.diff(self : Tensor,
n : int=1,
dim : int=-1,
prepend : Optional[Tensor],
append : Optional[Tensor],
out : Tensor) -> Tensor
torch.digamma(self : Tensor) -> Tensor
torch.digamma(self : Tensor,
out : Tensor) -> Tensor
torch.dist(self : Tensor,
other : Tensor,
p : number=2) -> Tensor
torch.div(self : Tensor,
other : Tensor) -> Tensor
torch.div(self : Tensor,
other : number) -> Tensor
torch.div(self : Tensor,
other : Tensor,
rounding_mode : Optional[str]) -> Tensor
torch.div(self : Tensor,
other : number,
rounding_mode : Optional[str]) -> Tensor
torch.div(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.div(self : Tensor,
other : Tensor,
rounding_mode : Optional[str],
out : Tensor) -> Tensor
torch.div(a : int,
b : int) -> float
torch.div(a : complex,
b : complex) -> complex
torch.div(a : float,
b : float) -> float
torch.div(a : number,
b : number) -> float
torch.divide(self : Tensor,
other : Tensor) -> Tensor
torch.divide(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.divide(self : Tensor,
other : number) -> Tensor
torch.divide(self : Tensor,
other : Tensor,
rounding_mode : Optional[str]) -> Tensor
torch.divide(self : Tensor,
other : Tensor,
rounding_mode : Optional[str],
out : Tensor) -> Tensor
torch.divide(self : Tensor,
other : number,
rounding_mode : Optional[str]) -> Tensor
torch.dot(self : Tensor,
tensor : Tensor) -> Tensor
torch.dot(self : Tensor,
tensor : Tensor,
out : Tensor) -> Tensor
torch.dropout(input : Tensor,
p : float,
train : bool) -> Tensor
torch.dropout_(self : Tensor,
p : float,
train : bool) -> Tensor
torch.dsplit(self : Tensor,
sections : int) -> List[Tensor]
torch.dsplit(self : Tensor,
indices : List[int]) -> List[Tensor]
torch.dstack(tensors : List[Tensor]) -> Tensor
torch.dstack(tensors : List[Tensor],
out : Tensor) -> Tensor
torch.eig(self : Tensor,
eigenvectors : bool=False) -> Tuple[Tensor, Tensor]
torch.eig(self : Tensor,
eigenvectors : bool=False,
e : Tensor,
v : Tensor) -> Tuple[Tensor, Tensor]
torch.einsum(equation : str,
tensors : List[Tensor]) -> Tensor
torch.einsum(a : Tensor) -> Tensor
torch.embedding(weight : Tensor,
indices : Tensor,
padding_idx : int=-1,
scale_grad_by_freq : bool=False,
sparse : bool=False) -> Tensor
torch.embedding_bag(weight : Tensor,
indices : Tensor,
offsets : Tensor,
scale_grad_by_freq : bool=False,
mode : int=0,
sparse : bool=False,
per_sample_weights : Optional[Tensor],
include_last_offset : bool=False) -> Tuple[Tensor, Tensor, Tensor, Tensor]
torch.embedding_bag(weight : Tensor,
indices : Tensor,
offsets : Tensor,
scale_grad_by_freq : bool,
mode : int,
sparse : bool,
per_sample_weights : Optional[Tensor],
include_last_offset : bool,
padding_idx : Optional[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor]
torch.embedding_renorm_(self : Tensor,
indices : Tensor,
max_norm : float,
norm_type : float) -> Tensor
torch.empty(size : List[int],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool],
memory_format : Optional[int]) -> Tensor
torch.empty(size : List[int],
memory_format : Optional[int],
out : Tensor) -> Tensor
torch.empty(size : List[int],
names : Optional[List[str]],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool],
memory_format : Optional[int]) -> Tensor
torch.empty_like(self : Tensor,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool],
memory_format : Optional[int]) -> Tensor
torch.empty_quantized(size : List[int],
qtensor : Tensor,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool],
memory_format : Optional[int]) -> Tensor
torch.empty_strided(size : List[int],
stride : List[int],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.eq(self : Tensor,
other : Tensor) -> Tensor
torch.eq(self : Tensor,
other : number) -> Tensor
torch.eq(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.eq(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.eq(a : List[int],
b : List[int]) -> bool
torch.eq(a : Device,
b : Device) -> bool
torch.eq(a : bool,
b : bool) -> bool
torch.eq(a : AnyEnumType,
b : AnyEnumType) -> bool
torch.eq(a : int,
b : int) -> bool
torch.eq(a : complex,
b : complex) -> bool
torch.eq(a : float,
b : float) -> bool
torch.eq(a : int,
b : float) -> bool
torch.eq(a : float,
b : int) -> bool
torch.eq(a : float,
b : complex) -> bool
torch.eq(a : complex,
b : float) -> bool
torch.eq(a : number,
b : number) -> bool
torch.eq(a : str,
b : str) -> bool
torch.eq(a : List[float],
b : List[float]) -> bool
torch.eq(a : List[Tensor],
b : List[Tensor]) -> bool
torch.eq(a : List[bool],
b : List[bool]) -> bool
torch.eq(a : List[str],
b : List[str]) -> bool
torch.equal(self : Tensor,
other : Tensor) -> bool
torch.erf(self : Tensor) -> Tensor
torch.erf(self : Tensor,
out : Tensor) -> Tensor
torch.erf(a : int) -> float
torch.erf(a : float) -> float
torch.erf(a : number) -> number
torch.erf_(self : Tensor) -> Tensor
torch.erfc(self : Tensor) -> Tensor
torch.erfc(self : Tensor,
out : Tensor) -> Tensor
torch.erfc(a : int) -> float
torch.erfc(a : float) -> float
torch.erfc(a : number) -> number
torch.erfc_(self : Tensor) -> Tensor
torch.erfinv(self : Tensor) -> Tensor
torch.erfinv(self : Tensor,
out : Tensor) -> Tensor
torch.exp(self : Tensor) -> Tensor
torch.exp(self : Tensor,
out : Tensor) -> Tensor
torch.exp(a : int) -> float
torch.exp(a : float) -> float
torch.exp(a : complex) -> complex
torch.exp(a : number) -> number
torch.exp2(self : Tensor) -> Tensor
torch.exp2(self : Tensor,
out : Tensor) -> Tensor
torch.exp2_(self : Tensor) -> Tensor
torch.exp_(self : Tensor) -> Tensor
torch.expm1(self : Tensor) -> Tensor
torch.expm1(self : Tensor,
out : Tensor) -> Tensor
torch.expm1(a : int) -> float
torch.expm1(a : float) -> float
torch.expm1(a : number) -> number
torch.expm1_(self : Tensor) -> Tensor
torch.eye(n : int,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.eye(n : int,
m : int,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.eye(n : int,
out : Tensor) -> Tensor
torch.eye(n : int,
m : int,
out : Tensor) -> Tensor
torch.fake_quantize_per_channel_affine(self : Tensor,
scale : Tensor,
zero_point : Tensor,
axis : int,
quant_min : int,
quant_max : int) -> Tensor
torch.fake_quantize_per_tensor_affine(self : Tensor,
scale : float,
zero_point : int,
quant_min : int,
quant_max : int) -> Tensor
torch.fake_quantize_per_tensor_affine(self : Tensor,
scale : Tensor,
zero_point : Tensor,
quant_min : int,
quant_max : int) -> Tensor
torch.fbgemm_linear_fp16_weight(input : Tensor,
packed_weight : Tensor,
bias : Tensor) -> Tensor
torch.fbgemm_linear_fp16_weight_fp32_activation(input : Tensor,
packed_weight : Tensor,
bias : Tensor) -> Tensor
torch.fbgemm_linear_int8_weight(input : Tensor,
weight : Tensor,
packed : Tensor,
col_offsets : Tensor,
weight_scale : number,
weight_zero_point : number,
bias : Tensor) -> Tensor
torch.fbgemm_linear_int8_weight_fp32_activation(input : Tensor,
weight : Tensor,
packed : Tensor,
col_offsets : Tensor,
weight_scale : number,
weight_zero_point : number,
bias : Tensor) -> Tensor
torch.fbgemm_linear_quantize_weight(input : Tensor) -> Tuple[Tensor, Tensor, float, int]
torch.fbgemm_pack_gemm_matrix_fp16(input : Tensor) -> Tensor
torch.fbgemm_pack_quantized_matrix(input : Tensor) -> Tensor
torch.fbgemm_pack_quantized_matrix(input : Tensor,
K : int,
N : int) -> Tensor
torch.feature_alpha_dropout(input : Tensor,
p : float,
train : bool) -> Tensor
torch.feature_alpha_dropout_(self : Tensor,
p : float,
train : bool) -> Tensor
torch.feature_dropout(input : Tensor,
p : float,
train : bool) -> Tensor
torch.feature_dropout_(self : Tensor,
p : float,
train : bool) -> Tensor
torch.fill_(self : Tensor,
value : number) -> Tensor
torch.fill_(self : Tensor,
value : Tensor) -> Tensor
torch.fix(self : Tensor) -> Tensor
torch.fix(self : Tensor,
out : Tensor) -> Tensor
torch.fix_(self : Tensor) -> Tensor
torch.flatten(self : Tensor,
dims : List[str],
out_dim : str) -> Tensor
torch.flatten(self : Tensor,
start_dim : int,
end_dim : int,
out_dim : str) -> Tensor
torch.flatten(self : Tensor,
start_dim : int=0,
end_dim : int=-1) -> Tensor
torch.flatten(self : Tensor,
start_dim : str,
end_dim : str,
out_dim : str) -> Tensor
torch.flip(self : Tensor,
dims : List[int]) -> Tensor
torch.fliplr(self : Tensor) -> Tensor
torch.flipud(self : Tensor) -> Tensor
torch.float_power(self : Tensor,
exponent : Tensor) -> Tensor
torch.float_power(self : Tensor,
exponent : Tensor,
out : Tensor) -> Tensor
torch.float_power(self : number,
exponent : Tensor) -> Tensor
torch.float_power(self : number,
exponent : Tensor,
out : Tensor) -> Tensor
torch.float_power(self : Tensor,
exponent : number) -> Tensor
torch.float_power(self : Tensor,
exponent : number,
out : Tensor) -> Tensor
torch.floor(self : Tensor) -> Tensor
torch.floor(self : Tensor,
out : Tensor) -> Tensor
torch.floor(a : int) -> int
torch.floor(a : float) -> int
torch.floor(a : number) -> number
torch.floor_(self : Tensor) -> Tensor
torch.floor_divide(self : Tensor,
other : Tensor) -> Tensor
torch.floor_divide(self : Tensor,
other : number) -> Tensor
torch.floor_divide(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.fmax(self : Tensor,
other : Tensor) -> Tensor
torch.fmax(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.fmin(self : Tensor,
other : Tensor) -> Tensor
torch.fmin(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.fmod(self : Tensor,
other : Tensor) -> Tensor
torch.fmod(self : Tensor,
other : number) -> Tensor
torch.fmod(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.fmod(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.fmod(a : int,
b : int) -> float
torch.fmod(a : float,
b : float) -> float
torch.fmod(a : int,
b : float) -> float
torch.fmod(a : float,
b : int) -> float
torch.fmod(a : number,
b : number) -> float
torch.frac(self : Tensor) -> Tensor
torch.frac(self : Tensor,
out : Tensor) -> Tensor
torch.frac_(self : Tensor) -> Tensor
torch.frexp(self : Tensor,
mantissa : Tensor,
exponent : Tensor) -> Tuple[Tensor, Tensor]
torch.frexp(self : Tensor) -> Tuple[Tensor, Tensor]
torch.frexp(a : float) -> Tuple[float, int]
torch.frobenius_norm(self : Tensor) -> Tensor
torch.frobenius_norm(self : Tensor,
dim : List[int],
keepdim : bool=False) -> Tensor
torch.frobenius_norm(self : Tensor,
dim : List[int],
keepdim : bool=False,
out : Tensor) -> Tensor
torch.from_file(filename : str,
shared : Optional[bool],
size : Optional[int]=0,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.full(size : List[int],
fill_value : number,
names : Optional[List[str]],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.full(size : List[int],
fill_value : number,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.full(size : List[int],
fill_value : number,
out : Tensor) -> Tensor
torch.full_like(self : Tensor,
fill_value : number,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool],
memory_format : Optional[int]) -> Tensor
torch.fused_moving_avg_obs_fake_quant(self : Tensor,
observer_on : Tensor,
fake_quant_on : Tensor,
running_min : Tensor,
running_max : Tensor,
scale : Tensor,
zero_point : Tensor,
averaging_const : float,
quant_min : int,
quant_max : int,
ch_axis : int,
per_row_fake_quant : bool=False,
symmetric_quant : bool=False) -> Tensor
torch.gather(self : Tensor,
dim : int,
index : Tensor,
sparse_grad : bool=False) -> Tensor
torch.gather(self : Tensor,
dim : int,
index : Tensor,
sparse_grad : bool=False,
out : Tensor) -> Tensor
torch.gather(self : Tensor,
dim : str,
index : Tensor,
sparse_grad : bool=False) -> Tensor
torch.gather(self : Tensor,
dim : str,
index : Tensor,
sparse_grad : bool=False,
out : Tensor) -> Tensor
torch.gcd(self : Tensor,
other : Tensor) -> Tensor
torch.gcd(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.gcd(a : int,
b : int) -> int
torch.gcd_(self : Tensor,
other : Tensor) -> Tensor
torch.ge(self : Tensor,
other : Tensor) -> Tensor
torch.ge(self : Tensor,
other : number) -> Tensor
torch.ge(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.ge(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.ge(a : int,
b : int) -> bool
torch.ge(a : float,
b : float) -> bool
torch.ge(a : int,
b : float) -> bool
torch.ge(a : float,
b : int) -> bool
torch.ge(a : number,
b : number) -> bool
torch.ge(a : str,
b : str) -> bool
torch.geqrf(self : Tensor) -> Tuple[Tensor, Tensor]
torch.geqrf(self : Tensor,
a : Tensor,
tau : Tensor) -> Tuple[Tensor, Tensor]
torch.ger(self : Tensor,
vec2 : Tensor) -> Tensor
torch.ger(self : Tensor,
vec2 : Tensor,
out : Tensor) -> Tensor
torch.get_device(self : Tensor) -> int
torch.gradient(self : Tensor,
spacing : Optional[number],
dim : Optional[int],
edge_order : int=1) -> List[Tensor]
torch.gradient(self : Tensor,
spacing : number,
dim : List[int],
edge_order : int=1) -> List[Tensor]
torch.gradient(self : Tensor,
dim : List[int],
edge_order : int=1) -> List[Tensor]
torch.gradient(self : Tensor,
spacing : List[number],
dim : Optional[int],
edge_order : int=1) -> List[Tensor]
torch.gradient(self : Tensor,
spacing : List[number],
dim : List[int],
edge_order : int=1) -> List[Tensor]
torch.gradient(self : Tensor,
spacing : List[Tensor],
dim : Optional[int],
edge_order : int=1) -> List[Tensor]
torch.gradient(self : Tensor,
spacing : List[Tensor],
dim : List[int],
edge_order : int=1) -> List[Tensor]
torch.greater(self : Tensor,
other : number) -> Tensor
torch.greater(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.greater(self : Tensor,
other : Tensor) -> Tensor
torch.greater(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.greater_equal(self : Tensor,
other : number) -> Tensor
torch.greater_equal(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.greater_equal(self : Tensor,
other : Tensor) -> Tensor
torch.greater_equal(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.grid_sampler(input : Tensor,
grid : Tensor,
interpolation_mode : int,
padding_mode : int,
align_corners : bool) -> Tensor
torch.grid_sampler_2d(input : Tensor,
grid : Tensor,
interpolation_mode : int,
padding_mode : int,
align_corners : bool) -> Tensor
torch.grid_sampler_3d(input : Tensor,
grid : Tensor,
interpolation_mode : int,
padding_mode : int,
align_corners : bool) -> Tensor
torch.group_norm(input : Tensor,
num_groups : int,
weight : Optional[Tensor],
bias : Optional[Tensor],
eps : float=1e-05,
cudnn_enabled : bool=True) -> Tensor
torch.gru(input : Tensor,
hx : Tensor,
params : List[Tensor],
has_biases : bool,
num_layers : int,
dropout : float,
train : bool,
bidirectional : bool,
batch_first : bool) -> Tuple[Tensor, Tensor]
torch.gru(data : Tensor,
batch_sizes : Tensor,
hx : Tensor,
params : List[Tensor],
has_biases : bool,
num_layers : int,
dropout : float,
train : bool,
bidirectional : bool) -> Tuple[Tensor, Tensor]
torch.gru_cell(input : Tensor,
hx : Tensor,
w_ih : Tensor,
w_hh : Tensor,
b_ih : Optional[Tensor],
b_hh : Optional[Tensor]) -> Tensor
torch.gt(self : Tensor,
other : Tensor) -> Tensor
torch.gt(self : Tensor,
other : number) -> Tensor
torch.gt(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.gt(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.gt(a : int,
b : int) -> bool
torch.gt(a : float,
b : float) -> bool
torch.gt(a : int,
b : float) -> bool
torch.gt(a : float,
b : int) -> bool
torch.gt(a : number,
b : number) -> bool
torch.gt(a : str,
b : str) -> bool
torch.hamming_window(window_length : int,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.hamming_window(window_length : int,
periodic : bool,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.hamming_window(window_length : int,
periodic : bool,
alpha : float,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.hamming_window(window_length : int,
periodic : bool,
alpha : float,
beta : float,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.hann_window(window_length : int,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.hann_window(window_length : int,
periodic : bool,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.hardshrink(self : Tensor,
lambd : number=0.5) -> Tensor
torch.hardshrink(self : Tensor,
lambd : number=0.5,
out : Tensor) -> Tensor
torch.heaviside(self : Tensor,
values : Tensor) -> Tensor
torch.heaviside(self : Tensor,
values : Tensor,
out : Tensor) -> Tensor
torch.hinge_embedding_loss(self : Tensor,
target : Tensor,
margin : float=1.0,
reduction : int=1) -> Tensor
torch.histc(self : Tensor,
bins : int=100,
min : number=0,
max : number=0) -> Tensor
torch.histc(self : Tensor,
bins : int=100,
min : number=0,
max : number=0,
out : Tensor) -> Tensor
torch.histogram(self : Tensor,
bins : Tensor,
weight : Optional[Tensor],
density : bool=False) -> Tuple[Tensor, Tensor]
torch.histogram(self : Tensor,
bins : Tensor,
weight : Optional[Tensor],
density : bool=False,
hist : Tensor,
bin_edges : Tensor) -> Tuple[Tensor, Tensor]
torch.histogram(self : Tensor,
bins : int=100,
range : Optional[List[float]],
weight : Optional[Tensor],
density : bool=False) -> Tuple[Tensor, Tensor]
torch.histogram(self : Tensor,
bins : int=100,
range : Optional[List[float]],
weight : Optional[Tensor],
density : bool=False,
hist : Tensor,
bin_edges : Tensor) -> Tuple[Tensor, Tensor]
torch.hsplit(self : Tensor,
sections : int) -> List[Tensor]
torch.hsplit(self : Tensor,
indices : List[int]) -> List[Tensor]
torch.hspmm(mat1 : Tensor,
mat2 : Tensor,
out : Tensor) -> Tensor
torch.hspmm(mat1 : Tensor,
mat2 : Tensor) -> Tensor
torch.hstack(tensors : List[Tensor]) -> Tensor
torch.hstack(tensors : List[Tensor],
out : Tensor) -> Tensor
torch.hypot(self : Tensor,
other : Tensor) -> Tensor
torch.hypot(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.i0(self : Tensor) -> Tensor
torch.i0(self : Tensor,
out : Tensor) -> Tensor
torch.i0_(self : Tensor) -> Tensor
torch.igamma(self : Tensor,
other : Tensor) -> Tensor
torch.igamma(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.igammac(self : Tensor,
other : Tensor) -> Tensor
torch.igammac(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.imag(self : Tensor) -> Tensor
torch.index_add(self : Tensor,
dim : int,
index : Tensor,
source : Tensor) -> Tensor
torch.index_add(self : Tensor,
dim : int,
index : Tensor,
source : Tensor,
alpha : number) -> Tensor
torch.index_add(self : Tensor,
dim : str,
index : Tensor,
source : Tensor,
alpha : number=1) -> Tensor
torch.index_copy(self : Tensor,
dim : int,
index : Tensor,
source : Tensor) -> Tensor
torch.index_copy(self : Tensor,
dim : str,
index : Tensor,
source : Tensor) -> Tensor
torch.index_fill(self : Tensor,
dim : str,
index : Tensor,
value : number) -> Tensor
torch.index_fill(self : Tensor,
dim : str,
index : Tensor,
value : Tensor) -> Tensor
torch.index_fill(self : Tensor,
dim : int,
index : Tensor,
value : number) -> Tensor
torch.index_fill(self : Tensor,
dim : int,
index : Tensor,
value : Tensor) -> Tensor
torch.index_put(self : Tensor,
indices : List[Optional[Tensor]],
values : Tensor,
accumulate : bool=False) -> Tensor
torch.index_put(self : Tensor,
indices : List[Tensor],
values : Tensor,
accumulate : bool=False) -> Tensor
torch.index_put_(self : Tensor,
indices : List[Optional[Tensor]],
values : Tensor,
accumulate : bool=False) -> Tensor
torch.index_put_(self : Tensor,
indices : List[Tensor],
values : Tensor,
accumulate : bool=False) -> Tensor
torch.index_select(self : Tensor,
dim : int,
index : Tensor) -> Tensor
torch.index_select(self : Tensor,
dim : int,
index : Tensor,
out : Tensor) -> Tensor
torch.index_select(self : Tensor,
dim : str,
index : Tensor) -> Tensor
torch.index_select(self : Tensor,
dim : str,
index : Tensor,
out : Tensor) -> Tensor
torch.inner(self : Tensor,
other : Tensor) -> Tensor
torch.inner(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.instance_norm(input : Tensor,
weight : Optional[Tensor],
bias : Optional[Tensor],
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
use_input_stats : bool,
momentum : float,
eps : float,
cudnn_enabled : bool) -> Tensor
torch.int_repr(self : Tensor) -> Tensor
torch.inverse(self : Tensor) -> Tensor
torch.inverse(self : Tensor,
out : Tensor) -> Tensor
torch.is_complex(self : Tensor) -> bool
torch.is_conj(self : Tensor) -> bool
torch.is_distributed(self : Tensor) -> bool
torch.is_floating_point(self : Tensor) -> bool
torch.is_grad_enabled() -> bool
torch.is_inference(self : Tensor) -> bool
torch.is_neg(self : Tensor) -> bool
torch.is_nonzero(self : Tensor) -> bool
torch.is_same_size(self : Tensor,
other : Tensor) -> bool
torch.is_signed(self : Tensor) -> bool
torch.is_vulkan_available() -> bool
torch.isclose(self : Tensor,
other : Tensor,
rtol : float=1e-05,
atol : float=1e-08,
equal_nan : bool=False) -> Tensor
torch.isfinite(self : Tensor) -> Tensor
torch.isfinite(a : float) -> bool
torch.isfinite(a : complex) -> bool
torch.isin(elements : Tensor,
test_elements : Tensor,
assume_unique : bool=False,
invert : bool=False) -> Tensor
torch.isin(elements : Tensor,
test_elements : Tensor,
assume_unique : bool=False,
invert : bool=False,
out : Tensor) -> Tensor
torch.isin(elements : Tensor,
test_element : number,
assume_unique : bool=False,
invert : bool=False) -> Tensor
torch.isin(elements : Tensor,
test_element : number,
assume_unique : bool=False,
invert : bool=False,
out : Tensor) -> Tensor
torch.isin(element : number,
test_elements : Tensor,
assume_unique : bool=False,
invert : bool=False) -> Tensor
torch.isin(element : number,
test_elements : Tensor,
assume_unique : bool=False,
invert : bool=False,
out : Tensor) -> Tensor
torch.isinf(self : Tensor) -> Tensor
torch.isinf(a : float) -> bool
torch.isinf(a : complex) -> bool
torch.isnan(self : Tensor) -> Tensor
torch.isnan(a : float) -> bool
torch.isnan(a : complex) -> bool
torch.isneginf(self : Tensor) -> Tensor
torch.isneginf(self : Tensor,
out : Tensor) -> Tensor
torch.isposinf(self : Tensor) -> Tensor
torch.isposinf(self : Tensor,
out : Tensor) -> Tensor
torch.isreal(self : Tensor) -> Tensor
torch.kaiser_window(window_length : int,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.kaiser_window(window_length : int,
periodic : bool,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.kaiser_window(window_length : int,
periodic : bool,
beta : float,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.kl_div(self : Tensor,
target : Tensor,
reduction : int=1,
log_target : bool=False) -> Tensor
torch.kron(self : Tensor,
other : Tensor) -> Tensor
torch.kron(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.kthvalue(self : Tensor,
k : int,
dim : int=-1,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.kthvalue(self : Tensor,
k : int,
dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.kthvalue(self : Tensor,
k : int,
dim : str,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.kthvalue(self : Tensor,
k : int,
dim : int=-1,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.layer_norm(input : Tensor,
normalized_shape : List[int],
weight : Optional[Tensor],
bias : Optional[Tensor],
eps : float=1e-05,
cudnn_enable : bool=True) -> Tensor
torch.lcm(self : Tensor,
other : Tensor) -> Tensor
torch.lcm(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.lcm_(self : Tensor,
other : Tensor) -> Tensor
torch.ldexp(self : Tensor,
other : Tensor) -> Tensor
torch.ldexp(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.ldexp(x : float,
i : int) -> float
torch.ldexp_(self : Tensor,
other : Tensor) -> Tensor
torch.le(self : Tensor,
other : Tensor) -> Tensor
torch.le(self : Tensor,
other : number) -> Tensor
torch.le(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.le(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.le(a : int,
b : int) -> bool
torch.le(a : float,
b : float) -> bool
torch.le(a : int,
b : float) -> bool
torch.le(a : float,
b : int) -> bool
torch.le(a : number,
b : number) -> bool
torch.le(a : str,
b : str) -> bool
torch.lerp(self : Tensor,
end : Tensor,
weight : number) -> Tensor
torch.lerp(self : Tensor,
end : Tensor,
weight : number,
out : Tensor) -> Tensor
torch.lerp(self : Tensor,
end : Tensor,
weight : Tensor) -> Tensor
torch.lerp(self : Tensor,
end : Tensor,
weight : Tensor,
out : Tensor) -> Tensor
torch.less(self : Tensor,
other : number) -> Tensor
torch.less(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.less(self : Tensor,
other : Tensor) -> Tensor
torch.less(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.less_equal(self : Tensor,
other : number) -> Tensor
torch.less_equal(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.less_equal(self : Tensor,
other : Tensor) -> Tensor
torch.less_equal(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.lgamma(self : Tensor) -> Tensor
torch.lgamma(self : Tensor,
out : Tensor) -> Tensor
torch.lgamma(a : int) -> float
torch.lgamma(a : float) -> float
torch.lgamma(a : number) -> number
torch.linspace(start : number,
end : number,
steps : Optional[int],
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.linspace(start : number,
end : number,
steps : Optional[int],
out : Tensor) -> Tensor
torch.log(self : Tensor) -> Tensor
torch.log(self : Tensor,
out : Tensor) -> Tensor
torch.log(a : int) -> float
torch.log(a : float) -> float
torch.log(a : complex) -> complex
torch.log(a : number) -> number
torch.log(a : int,
b : int) -> float
torch.log(a : float,
b : float) -> float
torch.log(a : complex,
b : complex) -> complex
torch.log(a : int,
b : float) -> float
torch.log(a : float,
b : int) -> float
torch.log(a : int,
b : complex) -> complex
torch.log(a : complex,
b : int) -> complex
torch.log(a : float,
b : complex) -> complex
torch.log(a : complex,
b : float) -> complex
torch.log(a : number,
b : number) -> float
torch.log10(self : Tensor) -> Tensor
torch.log10(self : Tensor,
out : Tensor) -> Tensor
torch.log10(a : int) -> float
torch.log10(a : float) -> float
torch.log10(a : complex) -> complex
torch.log10(a : number) -> number
torch.log10_(self : Tensor) -> Tensor
torch.log1p(self : Tensor) -> Tensor
torch.log1p(self : Tensor,
out : Tensor) -> Tensor
torch.log1p(a : int) -> float
torch.log1p(a : float) -> float
torch.log1p(a : number) -> number
torch.log1p_(self : Tensor) -> Tensor
torch.log2(self : Tensor) -> Tensor
torch.log2(self : Tensor,
out : Tensor) -> Tensor
torch.log2_(self : Tensor) -> Tensor
torch.log_(self : Tensor) -> Tensor
torch.log_softmax(self : Tensor,
dim : int,
dtype : Optional[int]) -> Tensor
torch.log_softmax(self : Tensor,
dim : str,
dtype : Optional[int]) -> Tensor
torch.logaddexp(self : Tensor,
other : Tensor) -> Tensor
torch.logaddexp(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.logaddexp2(self : Tensor,
other : Tensor) -> Tensor
torch.logaddexp2(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.logcumsumexp(self : Tensor,
dim : int) -> Tensor
torch.logcumsumexp(self : Tensor,
dim : str) -> Tensor
torch.logcumsumexp(self : Tensor,
dim : str,
out : Tensor) -> Tensor
torch.logcumsumexp(self : Tensor,
dim : int,
out : Tensor) -> Tensor
torch.logdet(self : Tensor) -> Tensor
torch.logical_and(self : Tensor,
other : Tensor) -> Tensor
torch.logical_and(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.logical_not(self : Tensor) -> Tensor
torch.logical_not(self : Tensor,
out : Tensor) -> Tensor
torch.logical_or(self : Tensor,
other : Tensor) -> Tensor
torch.logical_or(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.logical_xor(self : Tensor,
other : Tensor) -> Tensor
torch.logical_xor(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.logit(self : Tensor,
eps : Optional[float]) -> Tensor
torch.logit(self : Tensor,
eps : Optional[float],
out : Tensor) -> Tensor
torch.logit_(self : Tensor,
eps : Optional[float]) -> Tensor
torch.logspace(start : number,
end : number,
steps : Optional[int],
base : float=10.0,
dtype : Optional[int],
layout : Optional[int],
device : Optional[Device],
pin_memory : Optional[bool]) -> Tensor
torch.logspace(start : number,
end : number,
steps : Optional[int],
base : float=10.0,
out : Tensor) -> Tensor
torch.logsumexp(self : Tensor,
dim : List[int],
keepdim : bool=False) -> Tensor
torch.logsumexp(self : Tensor,
dim : List[str],
keepdim : bool=False) -> Tensor
torch.logsumexp(self : Tensor,
dim : List[str],
keepdim : bool=False,
out : Tensor) -> Tensor
torch.logsumexp(self : Tensor,
dim : List[int],
keepdim : bool=False,
out : Tensor) -> Tensor
torch.lstm(input : Tensor,
hx : List[Tensor],
params : List[Tensor],
has_biases : bool,
num_layers : int,
dropout : float,
train : bool,
bidirectional : bool,
batch_first : bool) -> Tuple[Tensor, Tensor, Tensor]
torch.lstm(data : Tensor,
batch_sizes : Tensor,
hx : List[Tensor],
params : List[Tensor],
has_biases : bool,
num_layers : int,
dropout : float,
train : bool,
bidirectional : bool) -> Tuple[Tensor, Tensor, Tensor]
torch.lstm_cell(input : Tensor,
hx : List[Tensor],
w_ih : Tensor,
w_hh : Tensor,
b_ih : Optional[Tensor],
b_hh : Optional[Tensor]) -> Tuple[Tensor, Tensor]
torch.lstsq(self : Tensor,
A : Tensor) -> Tuple[Tensor, Tensor]
torch.lstsq(self : Tensor,
A : Tensor,
X : Tensor,
qr : Tensor) -> Tuple[Tensor, Tensor]
torch.lt(self : Tensor,
other : Tensor) -> Tensor
torch.lt(self : Tensor,
other : number) -> Tensor
torch.lt(self : Tensor,
other : number,
out : Tensor) -> Tensor
torch.lt(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.lt(a : int,
b : int) -> bool
torch.lt(a : float,
b : float) -> bool
torch.lt(a : int,
b : float) -> bool
torch.lt(a : float,
b : int) -> bool
torch.lt(a : number,
b : number) -> bool
torch.lt(a : str,
b : str) -> bool
torch.lu_solve(self : Tensor,
LU_data : Tensor,
LU_pivots : Tensor) -> Tensor
torch.lu_solve(self : Tensor,
LU_data : Tensor,
LU_pivots : Tensor,
out : Tensor) -> Tensor
torch.lu_unpack(LU_data : Tensor,
LU_pivots : Tensor,
unpack_data : bool=True,
unpack_pivots : bool=True) -> Tuple[Tensor, Tensor, Tensor]
torch.lu_unpack(LU_data : Tensor,
LU_pivots : Tensor,
unpack_data : bool=True,
unpack_pivots : bool=True,
P : Tensor,
L : Tensor,
U : Tensor) -> Tuple[Tensor, Tensor, Tensor]
torch.manual_seed(seed : int) -> Tuple[]
torch.margin_ranking_loss(input1 : Tensor,
input2 : Tensor,
target : Tensor,
margin : float=0.0,
reduction : int=1) -> Tensor
torch.masked_fill(self : Tensor,
mask : Tensor,
value : number) -> Tensor
torch.masked_fill(self : Tensor,
mask : Tensor,
value : Tensor) -> Tensor
torch.masked_scatter(self : Tensor,
mask : Tensor,
source : Tensor) -> Tensor
torch.masked_select(self : Tensor,
mask : Tensor) -> Tensor
torch.masked_select(self : Tensor,
mask : Tensor,
out : Tensor) -> Tensor
torch.matmul(self : Tensor,
other : Tensor) -> Tensor
torch.matmul(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.matrix_exp(self : Tensor) -> Tensor
torch.matrix_power(self : Tensor,
n : int) -> Tensor
torch.matrix_power(self : Tensor,
n : int,
out : Tensor) -> Tensor
torch.matrix_rank(self : Tensor,
symmetric : bool=False) -> Tensor
torch.matrix_rank(self : Tensor,
tol : float,
symmetric : bool=False) -> Tensor
torch.max(self : Tensor) -> Tensor
torch.max(self : Tensor,
dim : int,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.max(self : Tensor,
dim : int,
keepdim : bool=False,
max : Tensor,
max_values : Tensor) -> Tuple[Tensor, Tensor]
torch.max(self : Tensor,
dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.max(self : Tensor,
dim : str,
keepdim : bool=False,
max : Tensor,
max_values : Tensor) -> Tuple[Tensor, Tensor]
torch.max(self : Tensor,
other : Tensor) -> Tensor
torch.max(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.max_pool1d(self : Tensor,
kernel_size : List[int],
stride : List[int]=[],
padding : List[int]=[0],
dilation : List[int]=[1],
ceil_mode : bool=False) -> Tensor
torch.max_pool1d_with_indices(self : Tensor,
kernel_size : List[int],
stride : List[int]=[],
padding : List[int]=[0],
dilation : List[int]=[1],
ceil_mode : bool=False) -> Tuple[Tensor, Tensor]
torch.max_pool2d(self : Tensor,
kernel_size : List[int],
stride : List[int]=[],
padding : List[int]=[0, 0],
dilation : List[int]=[1, 1],
ceil_mode : bool=False) -> Tensor
torch.max_pool3d(self : Tensor,
kernel_size : List[int],
stride : List[int]=[],
padding : List[int]=[0, 0, 0],
dilation : List[int]=[1, 1, 1],
ceil_mode : bool=False) -> Tensor
torch.maximum(self : Tensor,
other : Tensor) -> Tensor
torch.maximum(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.mean(self : Tensor,
dtype : Optional[int]) -> Tensor
torch.mean(self : Tensor,
dim : List[int],
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
torch.mean(self : Tensor,
dim : List[str],
keepdim : bool=False,
dtype : Optional[int]) -> Tensor
torch.mean(self : Tensor,
dim : List[str],
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
torch.mean(self : Tensor,
dim : List[int],
keepdim : bool=False,
dtype : Optional[int],
out : Tensor) -> Tensor
torch.median(self : Tensor) -> Tensor
torch.median(self : Tensor,
dim : int,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.median(self : Tensor,
dim : int,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.median(self : Tensor,
dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.median(self : Tensor,
dim : str,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.meshgrid(tensors : List[Tensor]) -> List[Tensor]
torch.meshgrid(tensors : List[Tensor],
indexing : str) -> List[Tensor]
torch.min(self : Tensor) -> Tensor
torch.min(self : Tensor,
dim : int,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.min(self : Tensor,
dim : int,
keepdim : bool=False,
min : Tensor,
min_indices : Tensor) -> Tuple[Tensor, Tensor]
torch.min(self : Tensor,
dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.min(self : Tensor,
dim : str,
keepdim : bool=False,
min : Tensor,
min_indices : Tensor) -> Tuple[Tensor, Tensor]
torch.min(self : Tensor,
other : Tensor) -> Tensor
torch.min(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.minimum(self : Tensor,
other : Tensor) -> Tensor
torch.minimum(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.miopen_batch_norm(input : Tensor,
weight : Tensor,
bias : Optional[Tensor],
running_mean : Optional[Tensor],
running_var : Optional[Tensor],
training : bool,
exponential_average_factor : float,
epsilon : float) -> Tuple[Tensor, Tensor, Tensor]
torch.miopen_convolution(self : Tensor,
weight : Tensor,
bias : Optional[Tensor],
padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool) -> Tensor
torch.miopen_convolution_transpose(self : Tensor,
weight : Tensor,
bias : Optional[Tensor],
padding : List[int],
output_padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool) -> Tensor
torch.miopen_depthwise_convolution(self : Tensor,
weight : Tensor,
bias : Optional[Tensor],
padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
benchmark : bool,
deterministic : bool) -> Tensor
torch.miopen_rnn(input : Tensor,
weight : List[Tensor],
weight_stride0 : int,
hx : Tensor,
cx : Optional[Tensor],
mode : int,
hidden_size : int,
num_layers : int,
batch_first : bool,
dropout : float,
train : bool,
bidirectional : bool,
batch_sizes : List[int],
dropout_state : Optional[Tensor]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]
torch.mkldnn_adaptive_avg_pool2d(self : Tensor,
output_size : List[int]) -> Tensor
torch.mkldnn_convolution(self : Tensor,
weight : Tensor,
bias : Optional[Tensor],
padding : List[int],
stride : List[int],
dilation : List[int],
groups : int) -> Tensor
torch.mkldnn_convolution_backward_weights(weight_size : List[int],
grad_output : Tensor,
self : Tensor,
padding : List[int],
stride : List[int],
dilation : List[int],
groups : int,
bias_defined : bool) -> Tuple[Tensor, Tensor]
torch.mkldnn_linear_backward_weights(grad_output : Tensor,
input : Tensor,
weight : Tensor,
bias_defined : bool) -> Tuple[Tensor, Tensor]
torch.mkldnn_max_pool2d(self : Tensor,
kernel_size : List[int],
stride : List[int]=[],
padding : List[int]=[0, 0],
dilation : List[int]=[1, 1],
ceil_mode : bool=False) -> Tensor
torch.mkldnn_max_pool3d(self : Tensor,
kernel_size : List[int],
stride : List[int]=[],
padding : List[int]=[0, 0, 0],
dilation : List[int]=[1, 1, 1],
ceil_mode : bool=False) -> Tensor
torch.mm(self : Tensor,
mat2 : Tensor) -> Tensor
torch.mm(self : Tensor,
mat2 : Tensor,
out : Tensor) -> Tensor
torch.mode(self : Tensor,
dim : int=-1,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.mode(self : Tensor,
dim : str,
keepdim : bool=False) -> Tuple[Tensor, Tensor]
torch.mode(self : Tensor,
dim : str,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.mode(self : Tensor,
dim : int=-1,
keepdim : bool=False,
values : Tensor,
indices : Tensor) -> Tuple[Tensor, Tensor]
torch.moveaxis(self : Tensor,
source : List[int],
destination : List[int]) -> Tensor
torch.moveaxis(self : Tensor,
source : int,
destination : int) -> Tensor
torch.movedim(self : Tensor,
source : List[int],
destination : List[int]) -> Tensor
torch.movedim(self : Tensor,
source : int,
destination : int) -> Tensor
torch.msort(self : Tensor) -> Tensor
torch.msort(self : Tensor,
out : Tensor) -> Tensor
torch.mul(self : Tensor,
other : Tensor) -> Tensor
torch.mul(self : Tensor,
other : number) -> Tensor
torch.mul(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.mul(l : List[t],
n : int) -> List[t]
torch.mul(n : int,
l : List[t]) -> List[t]
torch.mul(a : int,
b : int) -> int
torch.mul(a : complex,
b : complex) -> complex
torch.mul(a : float,
b : float) -> float
torch.mul(a : int,
b : complex) -> complex
torch.mul(a : complex,
b : int) -> complex
torch.mul(a : float,
b : complex) -> complex
torch.mul(a : complex,
b : float) -> complex
torch.mul(a : int,
b : float) -> float
torch.mul(a : float,
b : int) -> float
torch.mul(a : number,
b : number) -> number
torch.multinomial(self : Tensor,
num_samples : int,
replacement : bool=False,
generator : Optional[Generator]) -> Tensor
torch.multinomial(self : Tensor,
num_samples : int,
replacement : bool=False,
generator : Optional[Generator],
out : Tensor) -> Tensor
torch.multiply(self : Tensor,
other : Tensor) -> Tensor
torch.multiply(self : Tensor,
other : Tensor,
out : Tensor) -> Tensor
torch.multiply(self : Tensor,
other : number) -> Tensor
torch.mv(self : Tensor,
vec : Tensor) -> Tensor
torch.mv(self : Tensor,
vec : Tensor,
out : Tensor) -> Tensor
torch.mvlgamma(self : Tensor,
p : int,
out : Tensor) -> Tensor
torch.mvlgamma(self : Tensor,
p : int) -> Tensor
torch.nan_to_num(self : Tensor,
nan : Optional[float],
posinf : Optional[float],
neginf : Optional[float],
out : Tensor) -> Tensor
torch.nan_to_num(self : Tensor,
nan : Optional[float],
posinf : Optional[float],
neginf : Optional[float]) -> Tensor
torch.nan_to_num_(self : Tensor,
nan : Optional[float],
posinf : Optional[float],
neginf : Optional[float]) -