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Source code for torch.nn.modules.module

from collections import OrderedDict, namedtuple
import functools
import itertools
import warnings

import torch
from ..parameter import Parameter
import torch.utils.hooks as hooks

from torch import Tensor, device, dtype
from typing import Union, Tuple, Any, Callable, Iterator, Set, Optional, overload, TypeVar, Mapping, Dict
from ...utils.hooks import RemovableHandle

_grad_t = Union[Tuple[Tensor, ...], Tensor]
# See https://mypy.readthedocs.io/en/latest/generics.html#generic-methods-and-generic-self for the use
# of `T` to annotate `self`. Many methods of `Module` return `self` and we want those return values to be
# the type of the subclass, not the looser type of `Module`.
T = TypeVar('T', bound='Module')

class _IncompatibleKeys(namedtuple('IncompatibleKeys', ['missing_keys', 'unexpected_keys'])):
    def __repr__(self):
        if not self.missing_keys and not self.unexpected_keys:
            return '<All keys matched successfully>'
        return super(_IncompatibleKeys, self).__repr__()

    __str__ = __repr__


class ModuleAttributeError(AttributeError):
    """ When `__getattr__` raises AttributeError inside a property,
    AttributeError is raised with the property name instead of the
    attribute that initially raised AttributeError, making the error
    message uninformative. Using `ModuleAttributeError` instead
    fixes this issue."""


def _addindent(s_, numSpaces):
    s = s_.split('\n')
    # don't do anything for single-line stuff
    if len(s) == 1:
        return s_
    first = s.pop(0)
    s = [(numSpaces * ' ') + line for line in s]
    s = '\n'.join(s)
    s = first + '\n' + s
    return s


r"""This tracks hooks common to all modules that are executed before/after
calling forward and backward. This is global state used for debugging/profiling
purposes"""
_global_backward_hooks = OrderedDict()
_global_forward_pre_hooks = OrderedDict()
_global_forward_hooks = OrderedDict()


def register_module_forward_pre_hook(hook: Callable[..., None]) -> RemovableHandle:
    r"""Registers a forward pre-hook common to all modules.

    .. warning ::

        This adds global state to the `nn.module` module
        and it is only intended for debugging/profiling purposes.

    The hook will be called every time before :func:`forward` is invoked.
    It should have the following signature::

        hook(module, input) -> None or modified input

    The input contains only the positional arguments given to the module.
    Keyword arguments won't be passed to the hooks and only to the ``forward``.
    The hook can modify the input. User can either return a tuple or a
    single modified value in the hook. We will wrap the value into a tuple
    if a single value is returned(unless that value is already a tuple).

    This hook has precedence over the specific module hooks registered with
    ``register_forward_pre_hook``.

    Returns:
        :class:`torch.utils.hooks.RemovableHandle`:
            a handle that can be used to remove the added hook by calling
            ``handle.remove()``
    """
    handle = hooks.RemovableHandle(_global_forward_pre_hooks)
    _global_forward_pre_hooks[handle.id] = hook
    return handle


def register_module_forward_hook(hook: Callable[..., None]) -> RemovableHandle:
    r"""Registers a global forward hook for all the modules

    .. warning ::

        This adds global state to the `nn.module` module
        and it is only intended for debugging/profiling purposes.

    The hook will be called every time after :func:`forward` has computed an output.
    It should have the following signature::

        hook(module, input, output) -> None or modified output

    The input contains only the positional arguments given to the module.
    Keyword arguments won't be passed to the hooks and only to the ``forward``.
    The hook can modify the output. It can modify the input inplace but
    it will not have effect on forward since this is called after
    :func:`forward` is called.

    Returns:
        :class:`torch.utils.hooks.RemovableHandle`:
            a handle that can be used to remove the added hook by calling
            ``handle.remove()``

    This hook will be executed before specific module hooks registered with
    ``register_forward_hook``.
    """
    handle = hooks.RemovableHandle(_global_forward_hooks)
    _global_forward_hooks[handle.id] = hook
    return handle

def register_module_backward_hook(
    hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]]
) -> RemovableHandle:
    r"""Registers a backward hook common to all the modules.

    .. warning ::
        This adds global state to the `nn.module` module
        and it is only intended for debugging/profiling purposes.

        The current implementation will not have the presented behavior
        for complex :class:`Module` that perform many operations.
        In some failure cases, :attr:`grad_input` and :attr:`grad_output` will only
        contain the gradients for a subset of the inputs and outputs.
        For such :class:`Module`, you should use :func:`torch.Tensor.register_hook`
        directly on a specific input or output to get the required gradients.

    The hook will be called every time the gradients with respect to module
    inputs are computed. The hook should have the following signature::

        hook(module, grad_input, grad_output) -> Tensor or None

    The :attr:`grad_input` and :attr:`grad_output` may be tuples if the
    module has multiple inputs or outputs. The hook should not modify its
    arguments, but it can optionally return a new gradient with respect to
    input that will be used in place of :attr:`grad_input` in subsequent
    computations. :attr:`grad_input` will only correspond to the inputs given
    as positional arguments.

    Global hooks are called before hooks registered with `register_backward_hook`

    Returns:
        :class:`torch.utils.hooks.RemovableHandle`:
            a handle that can be used to remove the added hook by calling
            ``handle.remove()``

    """
    handle = hooks.RemovableHandle(_global_backward_hooks)
    _global_backward_hooks[handle.id] = hook
    return handle


# Trick mypy into not applying contravariance rules to inputs by defining
# forward as a value, rather than a function.  See also
# https://github.com/python/mypy/issues/8795
def _forward_unimplemented(self, *input: Any) -> None:
    r"""Defines the computation performed at every call.

    Should be overridden by all subclasses.

    .. note::
        Although the recipe for forward pass needs to be defined within
        this function, one should call the :class:`Module` instance afterwards
        instead of this since the former takes care of running the
        registered hooks while the latter silently ignores them.
    """
    raise NotImplementedError


class Module:
    r"""Base class for all neural network modules.

    Your models should also subclass this class.

    Modules can also contain other Modules, allowing to nest them in
    a tree structure. You can assign the submodules as regular attributes::

        import torch.nn as nn
        import torch.nn.functional as F

        class Model(nn.Module):
            def __init__(self):
                super(Model, self).__init__()
                self.conv1 = nn.Conv2d(1, 20, 5)
                self.conv2 = nn.Conv2d(20, 20, 5)

            def forward(self, x):
                x = F.relu(self.conv1(x))
                return F.relu(self.conv2(x))

    Submodules assigned in this way will be registered, and will have their
    parameters converted too when you call :meth:`to`, etc.

    :ivar training: Boolean represents whether this module is in training or
                    evaluation mode.
    :vartype training: bool
    """

    dump_patches: bool = False

    r"""This allows better BC support for :meth:`load_state_dict`. In
    :meth:`state_dict`, the version number will be saved as in the attribute
    `_metadata` of the returned state dict, and thus pickled. `_metadata` is a
    dictionary with keys that follow the naming convention of state dict. See
    ``_load_from_state_dict`` on how to use this information in loading.

    If new parameters/buffers are added/removed from a module, this number shall
    be bumped, and the module's `_load_from_state_dict` method can compare the
    version number and do appropriate changes if the state dict is from before
    the change."""
    _version: int = 1

    training: bool

    def __init__(self):
        """
        Initializes internal Module state, shared by both nn.Module and ScriptModule.
        """
        torch._C._log_api_usage_once("python.nn_module")

        self.training = True
        self._parameters = OrderedDict()
        self._buffers = OrderedDict()
        self._non_persistent_buffers_set = set()
        self._backward_hooks = OrderedDict()
        self._forward_hooks = OrderedDict()
        self._forward_pre_hooks = OrderedDict()
        self._state_dict_hooks = OrderedDict()
        self._load_state_dict_pre_hooks = OrderedDict()
        self._modules = OrderedDict()

    forward: Callable[..., Any] = _forward_unimplemented

    def register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None:
        r"""Adds a buffer to the module.

        This is typically used to register a buffer that should not to be
        considered a model parameter. For example, BatchNorm's ``running_mean``
        is not a parameter, but is part of the module's state. Buffers, by
        default, are persistent and will be saved alongside parameters. This
        behavior can be changed by setting :attr:`persistent` to ``False``. The
        only difference between a persistent buffer and a non-persistent buffer
        is that the latter will not be a part of this module's
        :attr:`state_dict`.

        Buffers can be accessed as attributes using given names.

        Args:
            name (string): name of the buffer. The buffer can be accessed
                from this module using the given name
            tensor (Tensor): buffer to be registered.
            persistent (bool): whether the buffer is part of this module's
                :attr:`state_dict`.

        Example::

            >>> self.register_buffer('running_mean', torch.zeros(num_features))

        """
        if persistent is False and isinstance(self, torch.jit.ScriptModule):
            raise RuntimeError("ScriptModule does not support non-persistent buffers")

        if '_buffers' not in self.__dict__:
            raise AttributeError(
                "cannot assign buffer before Module.__init__() call")
        elif not isinstance(name, torch._six.string_classes):
            raise TypeError("buffer name should be a string. "
                            "Got {}".format(torch.typename(name)))
        elif '.' in name:
            raise KeyError("buffer name can't contain \".\"")
        elif name == '':
            raise KeyError("buffer name can't be empty string \"\"")
        elif hasattr(self, name) and name not in self._buffers:
            raise KeyError("attribute '{}' already exists".format(name))
        elif tensor is not None and not isinstance(tensor, torch.Tensor):
            raise TypeError("cannot assign '{}' object to buffer '{}' "
                            "(torch Tensor or None required)"
                            .format(torch.typename(tensor), name))
        else:
            self._buffers[name] = tensor
            if persistent:
                self._non_persistent_buffers_set.discard(name)
            else:
                self._non_persistent_buffers_set.add(name)

    def register_parameter(self, name: str, param: Optional[Parameter]) -> None:
        r"""Adds a parameter to the module.

        The parameter can be accessed as an attribute using given name.

        Args:
            name (string): name of the parameter. The parameter can be accessed
                from this module using the given name
            param (Parameter): parameter to be added to the module.
        """
        if '_parameters' not in self.__dict__:
            raise AttributeError(
                "cannot assign parameter before Module.__init__() call")

        elif not isinstance(name, torch._six.string_classes):
            raise TypeError("parameter name should be a string. "
                            "Got {}".format(torch.typename(name)))
        elif '.' in name:
            raise KeyError("parameter name can't contain \".\"")
        elif name == '':
            raise KeyError("parameter name can't be empty string \"\"")
        elif hasattr(self, name) and name not in self._parameters:
            raise KeyError("attribute '{}' already exists".format(name))

        if param is None:
            self._parameters[name] = None
        elif not isinstance(param, Parameter):
            raise TypeError("cannot assign '{}' object to parameter '{}' "
                            "(torch.nn.Parameter or None required)"
                            .format(torch.typename(param), name))
        elif param.grad_fn:
            raise ValueError(
                "Cannot assign non-leaf Tensor to parameter '{0}'. Model "
                "parameters must be created explicitly. To express '{0}' "
                "as a function of another Tensor, compute the value in "
                "the forward() method.".format(name))
        else:
            self._parameters[name] = param

    def add_module(self, name: str, module: Optional['Module']) -> None:
        r"""Adds a child module to the current module.

        The module can be accessed as an attribute using the given name.

        Args:
            name (string): name of the child module. The child module can be
                accessed from this module using the given name
            module (Module): child module to be added to the module.
        """
        if not isinstance(module, Module) and module is not None:
            raise TypeError("{} is not a Module subclass".format(
                torch.typename(module)))
        elif not isinstance(name, torch._six.string_classes):
            raise TypeError("module name should be a string. Got {}".format(
                torch.typename(name)))
        elif hasattr(self, name) and name not in self._modules:
            raise KeyError("attribute '{}' already exists".format(name))
        elif '.' in name:
            raise KeyError("module name can't contain \".\", got: {}".format(name))
        elif name == '':
            raise KeyError("module name can't be empty string \"\"")
        self._modules[name] = module

    def _apply(self, fn):
        for module in self.children():
            module._apply(fn)

        def compute_should_use_set_data(tensor, tensor_applied):
            if torch._has_compatible_shallow_copy_type(tensor, tensor_applied):
                # If the new tensor has compatible tensor type as the existing tensor,
                # the current behavior is to change the tensor in-place using `.data =`,
                # and the future behavior is to overwrite the existing tensor. However,
                # changing the current behavior is a BC-breaking change, and we want it
                # to happen in future releases. So for now we introduce the
                # `torch.__future__.get_overwrite_module_params_on_conversion()`
                # global flag to let the user control whether they want the future
                # behavior of overwriting the existing tensor or not.
                return not torch.__future__.get_overwrite_module_params_on_conversion()
            else:
                return False

        for key, param in self._parameters.items():
            if param is not None:
                # Tensors stored in modules are graph leaves, and we don't want to
                # track autograd history of `param_applied`, so we have to use
                # `with torch.no_grad():`
                with torch.no_grad():
                    param_applied = fn(param)
                should_use_set_data = compute_should_use_set_data(param, param_applied)
                if should_use_set_data:
                    param.data = param_applied
                else:
                    assert isinstance(param, Parameter)
                    assert param.is_leaf
                    self._parameters[key] = Parameter(param_applied, param.requires_grad)

                if param.grad is not None:
                    with torch.no_grad():
                        grad_applied = fn(param.grad)
                    should_use_set_data = compute_should_use_set_data(param.grad, grad_applied)
                    if should_use_set_data:
                        param.grad.data = grad_applied
                    else:
                        assert param.grad.is_leaf
                        self._parameters[key].grad = grad_applied.requires_grad_(param.grad.requires_grad)

        for key, buf in self._buffers.items():
            if buf is not None:
                self._buffers[key] = fn(buf)

        return self

    def apply(self: T, fn: Callable[['Module'], None]) -> T:
        r"""Applies ``fn`` recursively to every submodule (as returned by ``.children()``)
        as well as self. Typical use includes initializing the parameters of a model
        (see also :ref:`nn-init-doc`).

        Args:
            fn (:class:`Module` -> None): function to be applied to each submodule

        Returns:
            Module: self

        Example::

            >>> @torch.no_grad()
            >>> def init_weights(m):
            >>>     print(m)
            >>>     if type(m) == nn.Linear:
            >>>         m.weight.fill_(1.0)
            >>>         print(m.weight)
            >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
            >>> net.apply(init_weights)
            Linear(in_features=2, out_features=2, bias=True)
            Parameter containing:
            tensor([[ 1.,  1.],
                    [ 1.,  1.]])
            Linear(in_features=2, out_features=2, bias=True)
            Parameter containing:
            tensor([[ 1.,  1.],
                    [ 1.,  1.]])
            Sequential(
              (0): Linear(in_features=2, out_features=2, bias=True)
              (1): Linear(in_features=2, out_features=2, bias=True)
            )
            Sequential(
              (0): Linear(in_features=2, out_features=2, bias=True)
              (1): Linear(in_features=2, out_features=2, bias=True)
            )
        """
        for module in self.children():
            module.apply(fn)
        fn(self)
        return self

    def cuda(self: T, device: Optional[Union[int, device]] = None) -> T:
        r"""Moves all model parameters and buffers to the GPU.

        This also makes associated parameters and buffers different objects. So
        it should be called before constructing optimizer if the module will
        live on GPU while being optimized.

        Arguments:
            device (int, optional): if specified, all parameters will be
                copied to that device

        Returns:
            Module: self
        """
        return self._apply(lambda t: t.cuda(device))

    def cpu(self: T) -> T:
        r"""Moves all model parameters and buffers to the CPU.

        Returns:
            Module: self
        """
        return self._apply(lambda t: t.cpu())

    def type(self: T, dst_type: Union[dtype, str]) -> T:
        r"""Casts all parameters and buffers to :attr:`dst_type`.

        Arguments:
            dst_type (type or string): the desired type

        Returns:
            Module: self
        """
        return self._apply(lambda t: t.type(dst_type))

    def float(self: T) -> T:
        r"""Casts all floating point parameters and buffers to float datatype.

        Returns:
            Module: self
        """
        return self._apply(lambda t: t.float() if t.is_floating_point() else t)

    def double(self: T) -> T:
        r"""Casts all floating point parameters and buffers to ``double`` datatype.

        Returns:
            Module: self
        """
        return self._apply(lambda t: t.double() if t.is_floating_point() else t)

    def half(self: T) -> T:
        r"""Casts all floating point parameters and buffers to ``half`` datatype.

        Returns:
            Module: self
        """
        return self._apply(lambda t: t.half() if t.is_floating_point() else t)

    def bfloat16(self: T) -> T:
        r"""Casts all floating point parameters and buffers to ``bfloat16`` datatype.

        Returns:
            Module: self
        """
        return self._apply(lambda t: t.bfloat16() if t.is_floating_point() else t)

    @overload
    def to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ...,
           non_blocking: bool = ...) -> T:
        ...

    @overload
    def to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...) -> T:
        ...

    @overload
    def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T:
        ...

    def to(self, *args, **kwargs):
        r"""Moves and/or casts the parameters and buffers.

        This can be called as

        .. function:: to(device=None, dtype=None, non_blocking=False)

        .. function:: to(dtype, non_blocking=False)

        .. function:: to(tensor, non_blocking=False)

        .. function:: to(memory_format=torch.channels_last)

        Its signature is similar to :meth:`torch.Tensor.to`, but only accepts
        floating point or complex :attr:`dtype`s. In addition, this method will
        only cast the floating point or complex parameters and buffers to :attr:`dtype`
        (if given). The integral parameters and buffers will be moved
        :attr:`device`, if that is given, but with dtypes unchanged. When
        :attr:`non_blocking` is set, it tries to convert/move asynchronously
        with respect to the host if possible, e.g., moving CPU Tensors with
        pinned memory to CUDA devices.

        See below for examples.

        .. note::
            This method modifies the module in-place.

        Args:
            device (:class:`torch.device`): the desired device of the parameters
                and buffers in this module
            dtype (:class:`torch.dtype`): the desired floating point or complex dtype of
                the parameters and buffers in this module
            tensor (torch.Tensor): Tensor whose dtype and device are the desired
                dtype and device for all parameters and buffers in this module
            memory_format (:class:`torch.memory_format`): the desired memory
                format for 4D parameters and buffers in this module (keyword
                only argument)

        Returns:
            Module: self

        Examples::

            >>> linear = nn.Linear(2, 2)
            >>> linear.weight
            Parameter containing:
            tensor([[ 0.1913, -0.3420],
                    [-0.5113, -0.2325]])
            >>> linear.to(torch.double)
            Linear(in_features=2, out_features=2, bias=True)
            >>> linear.weight
            Parameter containing:
            tensor([[ 0.1913, -0.3420],
                    [-0.5113, -0.2325]], dtype=torch.float64)
            >>> gpu1 = torch.device("cuda:1")
            >>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
            Linear(in_features=2, out_features=2, bias=True)
            >>> linear.weight
            Parameter containing:
            tensor([[ 0.1914, -0.3420],
                    [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
            >>> cpu = torch.device("cpu")
            >>> linear.to(cpu)
            Linear(in_features=2, out_features=2, bias=True)
            >>> linear.weight
            Parameter containing:
            tensor([[ 0.1914, -0.3420],
                    [-0.5112, -0.2324]], dtype=torch.float16)

            >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)
            >>> linear.weight
            Parameter containing:
            tensor([[ 0.3741+0.j,  0.2382+0.j],
                    [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)
            >>> linear(torch.ones(3, 2, dtype=torch.cdouble))
            tensor([[0.6122+0.j, 0.1150+0.j],
                    [0.6122+0.j, 0.1150+0.j],
                    [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)

        """

        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)

        if dtype is not None:
            if not (dtype.is_floating_point or dtype.is_complex):
                raise TypeError('nn.Module.to only accepts floating point or complex '
                                'dtypes, but got desired dtype={}'.format(dtype))
            if dtype.is_complex:
                warnings.warn(
                    "Complex modules are a new feature under active development whose design may change, "
                    "and some modules might not work as expected when using complex tensors as parameters or buffers. "
                    "Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.md "
                    "if a complex module does not work as expected.")

        def convert(t):
            if convert_to_format is not None and t.dim() == 4:
                return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None,
                            non_blocking, memory_format=convert_to_format)
            return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)

        return self._apply(convert)

    def register_backward_hook(
        self, hook: Callable[['Module', _grad_t, _grad_t], Union[None, Tensor]]
    ) -> RemovableHandle:
        r"""Registers a backward hook on the module.

        .. warning ::

            The current implementation will not have the presented behavior
            for complex :class:`Module` that perform many operations.
            In some failure cases, :attr:`grad_input` and :attr:`grad_output` will only
            contain the gradients for a subset of the inputs and outputs.
            For such :class:`Module`, you should use :func:`torch.Tensor.register_hook`
            directly on a specific input or output to get the required gradients.

        The hook will be called every time the gradients with respect to module
        inputs are computed. The hook should have the following signature::

            hook(module, grad_input, grad_output) -> Tensor or None

        The :attr:`grad_input` and :attr:`grad_output` may be tuples if the
        module has multiple inputs or outputs. The hook should not modify its
        arguments, but it can optionally return a new gradient with respect to
        input that will be used in place of :attr:`grad_input` in subsequent
        computations. :attr:`grad_input` will only correspond to the inputs given
        as positional arguments.

        Returns:
            :class:`torch.utils.hooks.RemovableHandle`:
                a handle that can be used to remove the added hook by calling
                ``handle.remove()``

        """
        handle = hooks.RemovableHandle(self._backward_hooks)
        self._backward_hooks[handle.id] = hook
        return handle

    def register_forward_pre_hook(self, hook: Callable[..., None]) -> RemovableHandle:
        r"""Registers a forward pre-hook on the module.

        The hook will be called every time before :func:`forward` is invoked.
        It should have the following signature::

            hook(module, input) -> None or modified input

        The input contains only the positional arguments given to the module.
        Keyword arguments won't be passed to the hooks and only to the ``forward``.
        The hook can modify the input. User can either return a tuple or a
        single modified value in the hook. We will wrap the value into a tuple
        if a single value is returned(unless that value is already a tuple).

        Returns:
            :class:`torch.utils.hooks.RemovableHandle`:
                a handle that can be used to remove the added hook by calling
                ``handle.remove()``
        """
        handle = hooks.RemovableHandle(self._forward_pre_hooks)
        self._forward_pre_hooks[handle.id] = hook
        return handle

    def register_forward_hook(self, hook: Callable[..., None]) -> RemovableHandle:
        r"""Registers a forward hook on the module.

        The hook will be called every time after :func:`forward` has computed an output.
        It should have the following signature::

            hook(module, input, output) -> None or modified output

        The input contains only the positional arguments given to the module.
        Keyword arguments won't be passed to the hooks and only to the ``forward``.
        The hook can modify the output. It can modify the input inplace but
        it will not have effect on forward since this is called after
        :func:`forward` is called.

        Returns:
            :class:`torch.utils.hooks.RemovableHandle`:
                a handle that can be used to remove the added hook by calling
                ``handle.remove()``
        """
        handle = hooks.RemovableHandle(self._forward_hooks)
        self._forward_hooks[handle.id] = hook
        return handle

    def _slow_forward(self, *input, **kwargs):
        tracing_state = torch._C._get_tracing_state()
        if not tracing_state or isinstance(self.forward, torch._C.ScriptMethod):
            return self.forward(*input, **kwargs)
        recording_scopes = torch.jit._trace._trace_module_map is not None
        if recording_scopes:
            name = torch.jit._trace._trace_module_map[self] if self in torch.jit._trace._trace_module_map else None
            if name:
                cur_scope_name = tracing_state.current_scope()
                tracing_state.push_scope(name)
            else:
                recording_scopes = False
        try:
            result = self.forward(*input, **kwargs)
        finally:
            if recording_scopes:
                tracing_state.pop_scope()
        return result

    def _call_impl(self, *input, **kwargs):
        for hook in itertools.chain(
                _global_forward_pre_hooks.values(),
                self._forward_pre_hooks.values()):
            result = hook(self, input)
            if result is not None:
                if not isinstance(result, tuple):
                    result = (result,)
                input = result
        if torch._C._get_tracing_state():
            result = self._slow_forward(*input, **kwargs)
        else:
            result = self.forward(*input, **kwargs)
        for hook in itertools.chain(
                _global_forward_hooks.values(),
                self._forward_hooks.values()):
            hook_result = hook(self, input, result)
            if hook_result is not None:
                result = hook_result
        if (len(self._backward_hooks) > 0) or (len(_global_backward_hooks) > 0):
            var = result
            while not isinstance(var, torch.Tensor):
                if isinstance(var, dict):
                    var = next((v for v in var.values() if isinstance(v, torch.Tensor)))
                else:
                    var = var[0]
            grad_fn = var.grad_fn
            if grad_fn is not None:
                for hook in itertools.chain(
                        _global_backward_hooks.values(),
                        self._backward_hooks.values()):
                    wrapper = functools.partial(hook, self)
                    functools.update_wrapper(wrapper, hook)
                    grad_fn.register_hook(wrapper)
        return result

    __call__ : Callable[..., Any] = _call_impl

    def __setstate__(self, state):
        self.__dict__.update(state)
        # Support loading old checkpoints that don't have the following attrs:
        if '_forward_pre_hooks' not in self.__dict__:
            self._forward_pre_hooks = OrderedDict()
        if '_state_dict_hooks' not in self.__dict__:
            self._state_dict_hooks = OrderedDict()
        if '_load_state_dict_pre_hooks' not in self.__dict__:
            self._load_state_dict_pre_hooks = OrderedDict()
        if '_non_persistent_buffers_set' not in self.__dict__:
            self._non_persistent_buffers_set = set()

    def __getattr__(self, name: str) -> Union[Tensor, 'Module']:
        if '_parameters' in self.__dict__:
            _parameters = self.__dict__['_parameters']
            if name in _parameters:
                return _parameters[name]
        if '_buffers' in self.__dict__:
            _buffers = self.__dict__['_buffers']
            if name in _buffers:
                return _buffers[name]
        if '_modules' in self.__dict__:
            modules = self.__dict__['_modules']
            if name in modules:
                return modules[name]
        raise ModuleAttributeError("'{}' object has no attribute '{}'".format(
            type(self).__name__, name))

    def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:
        def remove_from(*dicts_or_sets):
            for d in dicts_or_sets:
                if name in d:
                    if isinstance(d, dict):
                        del d[name]
                    else:
                        d.discard(name)

        params = self.__dict__.get('_parameters')
        if isinstance(value, Parameter):
            if params is None:
                raise AttributeError(
                    "cannot assign parameters before Module.__init__() call")
            remove_from(self.__dict__, self._buffers, self._modules, self._non_persistent_buffers_set)
            self.register_parameter(name, value)
        elif params is not None and name in params:
            if value is not None:
                raise TypeError("cannot assign '{}' as parameter '{}' "
                                "(torch.nn.Parameter or None expected)"
                                .format(torch.typename(value), name))
            self.register_parameter(name, value)
        else:
            modules = self.__dict__.get('_modules')
            if isinstance(value, Module):
                if modules is None:
                    raise AttributeError(
                        "cannot assign module before Module.__init__() call")
                remove_from(self.__dict__, self._parameters, self._buffers, self._non_persistent_buffers_set)
                modules[name] = value
            elif modules is not None and name in modules:
                if value is not None:
                    raise TypeError("cannot assign '{}' as child module '{}' "
                                    "(torch.nn.Module or None expected)"
                                    .format(torch.typename(value), name))
                modules[name] = value
            else:
                buffers = self.__dict__.get('_buffers')
                if buffers is not None and name in buffers:
                    if value is not None and not isinstance(value, torch.Tensor):
                        raise TypeError("cannot assign '{}' as buffer '{}' "
                                        "(torch.Tensor or None expected)"
                                        .format(torch.typename(value), name))
                    buffers[name] = value
                else:
                    object.__setattr__(self, name, value)

    def __delattr__(self, name):
        if name in self._parameters:
            del self._parameters[name]
        elif name in self._buffers:
            del self._buffers[name]
            self._non_persistent_buffers_set.discard(name)
        elif name in self._modules:
            del self._modules[name]
        else:
            object.__delattr__(self, name)

    def _register_state_dict_hook(self, hook):
        r"""These hooks will be called with arguments: `self`, `state_dict`,
        `prefix`, `local_metadata`, after the `state_dict` of `self` is set.
        Note that only parameters and buffers of `self` or its children are
        guaranteed to exist in `state_dict`. The hooks may modify `state_dict`
        inplace or return a new one.
        """
        handle = hooks.RemovableHandle(self._state_dict_hooks)
        self._state_dict_hooks[handle.id] = hook
        return handle

    def _save_to_state_dict(self, destination, prefix, keep_vars):
        r"""Saves module state to `destination` dictionary, containing a state
        of the module, but not its descendants. This is called on every
        submodule in :meth:`~torch.nn.Module.state_dict`.

        In rare cases, subclasses can achieve class-specific behavior by
        overriding this method with custom logic.

        Arguments:
            destination (dict): a dict where state will be stored
            prefix (str): the prefix for parameters and buffers used in this
                module
        """
        for name, param in self._parameters.items():
            if param is not None:
                destination[prefix + name] = param if keep_vars else param.detach()
        for name, buf in self._buffers.items():
            if buf is not None and name not in self._non_persistent_buffers_set:
                destination[prefix + name] = buf if keep_vars else buf.detach()

    # The user can pass an optional arbitrary mappable object to `state_dict`, in which case `state_dict` returns
    # back that same object. But if they pass nothing, an `OrederedDict` is created and returned.
    T_destination = TypeVar('T_destination', bound=Mapping[str, Tensor])

    @overload
    def state_dict(self, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination:
        ...

    # TODO: annotate with OrderedDict not Dict, but there is a problem:
    # https://docs.python.org/3/library/typing.html#typing.OrderedDict
    @overload
    def state_dict(self, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Tensor]:
        ...

    def state_dict(self, destination=None, prefix='', keep_vars=False):
        r"""Returns a dictionary containing a whole state of the module.

        Both parameters and persistent buffers (e.g. running averages) are
        included. Keys are corresponding parameter and buffer names.

        Returns:
            dict:
                a dictionary containing a whole state of the module

        Example::

            >>> module.state_dict().keys()
            ['bias', 'weight']

        """
        if destination is None:
            destination = OrderedDict()
            destination._metadata = OrderedDict()
        destination._metadata[prefix[:-1]] = local_metadata = dict(version=self._version)
        self._save_to_state_dict(destination, prefix, keep_vars)
        for name, module in self._modules.items():
            if module is not None:
                module.state_dict(destination, prefix + name + '.', keep_vars=keep_vars)
        for hook in self._state_dict_hooks.values():
            hook_result = hook(self, destination, prefix, local_metadata)
            if hook_result is not None:
                destination = hook_result
        return destination

    def _register_load_state_dict_pre_hook(self, hook):
        r"""These hooks will be called with arguments: `state_dict`, `prefix`,
        `local_metadata`, `strict`, `missing_keys`, `unexpected_keys`,
        `error_msgs`, before loading `state_dict` into `self`. These arguments
        are exactly the same as those of `_load_from_state_dict`.
        """
        handle = hooks.RemovableHandle(self._load_state_dict_pre_hooks)
        self._load_state_dict_pre_hooks[handle.id] = hook
        return handle

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        r"""Copies parameters and buffers from :attr:`state_dict` into only
        this module, but not its descendants. This is called on every submodule
        in :meth:`~torch.nn.Module.load_state_dict`. Metadata saved for this
        module in input :attr:`state_dict` is provided as :attr:`local_metadata`.
        For state dicts without metadata, :attr:`local_metadata` is empty.
        Subclasses can achieve class-specific backward compatible loading using
        the version number at `local_metadata.get("version", None)`.

        .. note::
            :attr:`state_dict` is not the same object as the input
            :attr:`state_dict` to :meth:`~torch.nn.Module.load_state_dict`. So
            it can be modified.

        Arguments:
            state_dict (dict): a dict containing parameters and
                persistent buffers.
            prefix (str): the prefix for parameters and buffers used in this
                module
            local_metadata (dict): a dict containing the metadata for this module.
                See
            strict (bool): whether to strictly enforce that the keys in
                :attr:`state_dict` with :attr:`prefix` match the names of
                parameters and buffers in this module
            missing_keys (list of str): if ``strict=True``, add missing keys to
                this list
            unexpected_keys (list of str): if ``strict=True``, add unexpected
                keys to this list
            error_msgs (list of str): error messages should be added to this
                list, and will be reported together in
                :meth:`~torch.nn.Module.load_state_dict`
        """
        for hook in self._load_state_dict_pre_hooks.values():
            hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)

        persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set}
        local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items())
        local_state = {k: v for k, v in local_name_params if v is not None}

        for name, param in local_state.items():
            key = prefix + name
            if key in state_dict:
                input_param = state_dict[key]
                # This is used to avoid copying uninitialized parameters into
                # non-lazy modules, since they dont have the hook to do the checks
                # in such case, it will error when accessing the .shape attribute.
                is_param_lazy = isinstance(param, torch.nn.parameter.UninitializedParameter)
                # Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+
                if not is_param_lazy and len(param.shape) == 0 and len(input_param.shape) == 1:
                    input_param = input_param[0]

                if not is_param_lazy and input_param.shape != param.shape:
                    # local shape should match the one in checkpoint
                    error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, '
                                      'the shape in current model is {}.'
                                      .format(key, input_param.shape, param.shape))
                    continue
                try:
                    with torch.no_grad():
                        param.copy_(input_param)
                except Exception as ex:
                    error_msgs.append('While copying the parameter named "{}", '
                                      'whose dimensions in the model are {} and '
                                      'whose dimensions in the checkpoint are {}, '
                                      'an exception occurred : {}.'
                                      .format(key, param.size(), input_param.size(), ex.args))
            elif strict:
                missing_keys.append(key)

        if strict:
            for key in state_dict.keys():
                if key.startswith(prefix):
                    input_name = key[len(prefix):]
                    input_name = input_name.split('.', 1)[0]  # get the name of param/buffer/child
                    if input_name not in self._modules and input_name not in local_state:
                        unexpected_keys.append(key)

    def load_state_dict(self, state_dict: Union[Dict[str, Tensor], Dict[str, Tensor]],
                        strict: bool = True):
        r"""Copies parameters and buffers from :attr:`state_dict` into
        this module and its descendants. If :attr:`strict` is ``True``, then
        the keys of :attr:`state_dict` must exactly match the keys returned
        by this module's :meth:`~torch.nn.Module.state_dict` function.

        Arguments:
            state_dict (dict): a dict containing parameters and
                persistent buffers.
            strict (bool, optional): whether to strictly enforce that the keys
                in :attr:`state_dict` match the keys returned by this module's
                :meth:`~torch.nn.Module.state_dict` function. Default: ``True``

        Returns:
            ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
                * **missing_keys** is a list of str containing the missing keys
                * **unexpected_keys** is a list of str containing the unexpected keys
        """
        missing_keys = []
        unexpected_keys = []
        error_msgs = []

        # copy state_dict so _load_from_state_dict can modify it
        metadata = getattr(state_dict, '_metadata', None)
        state_dict = state_dict.copy()
        if metadata is not None:
            state_dict._metadata = metadata

        def load(module, prefix=''):
            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
            module._load_from_state_dict(
                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
            for name, child in module._modules.items():
                if child is not None:
                    load(child, prefix + name + '.')

        load(self)
        load = None  # break load->load reference cycle

        if strict:
            if len(unexpected_keys) > 0:
                error_msgs.insert(
                    0, 'Unexpected key(s) in state_dict: {}. '.format(
                        ', '.join('"{}"'.format(k) for k in unexpected_keys)))
            if len(missing_keys) > 0:
                error_msgs.insert(
                    0, 'Missing key(s) in state_dict: {}. '.format(
                        ', '.join('"{}"'.format(k) for k in missing_keys)))

        if len(error_msgs) > 0:
            raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
                               self.__class__.__name__, "\n\t".join(error_msgs)))
        return _IncompatibleKeys(missing_keys, unexpected_keys)

    def _named_members(self, get_members_fn, prefix='', recurse=True):
        r"""Helper method for yielding various names + members of modules."""
        memo = set()
        modules = self.named_modules(prefix=prefix) if recurse else [(prefix, self)]
        for module_prefix, module in modules:
            members = get_members_fn(module)
            for k, v in members:
                if v is None or v in memo:
                    continue
                memo.add(v)
                name = module_prefix + ('.' if module_prefix else '') + k
                yield name, v

    def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
        r"""Returns an iterator over module parameters.

        This is typically passed to an optimizer.

        Args:
            recurse (bool): if True, then yields parameters of this module
                and all submodules. Otherwise, yields only parameters that
                are direct members of this module.

        Yields:
            Parameter: module parameter

        Example::

            >>> for param in model.parameters():
            >>>     print(type(param), param.size())
            <class 'torch.Tensor'> (20L,)
            <class 'torch.Tensor'> (20L, 1L, 5L, 5L)

        """
        for name, param in self.named_parameters(recurse=recurse):
            yield param

    def named_parameters(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Tensor]]:
        r"""Returns an iterator over module parameters, yielding both the
        name of the parameter as well as the parameter itself.

        Args:
            prefix (str): prefix to prepend to all parameter names.
            recurse (bool): if True, then yields parameters of this module
                and all submodules. Otherwise, yields only parameters that
                are direct members of this module.

        Yields:
            (string, Parameter): Tuple containing the name and parameter

        Example::

            >>> for name, param in self.named_parameters():
            >>>    if name in ['bias']:
            >>>        print(param.size())

        """
        gen = self._named_members(
            lambda module: module._parameters.items(),
            prefix=prefix, recurse=recurse)
        for elem in gen:
            yield elem

    def buffers(self, recurse: bool = True) -> Iterator[Tensor]:
        r"""Returns an iterator over module buffers.

        Args:
            recurse (bool): if True, then yields buffers of this module
                and all submodules. Otherwise, yields only buffers that
                are direct members of this module.

        Yields:
            torch.Tensor: module buffer

        Example::

            >>> for buf in model.buffers():
            >>>     print(type(buf), buf.size())
            <class 'torch.Tensor'> (20L,)
            <class 'torch.Tensor'> (20L, 1L, 5L, 5L)

        """
        for name, buf in self.named_buffers(recurse=recurse):
            yield buf

    def named_buffers(self, prefix: str = '', recurse: bool = True) -> Iterator[Tuple[str, Tensor]]:
        r"""Returns an iterator over module buffers, yielding both the
        name of the buffer as well as the buffer itself.

        Args:
            prefix (str): prefix to prepend to all buffer names.
            recurse (bool): if True, then yields buffers of this module
                and all submodules. Otherwise, yields only buffers that
                are direct members of this module.

        Yields:
            (string, torch.Tensor): Tuple containing the name and buffer

        Example::

            >>> for name, buf in self.named_buffers():
            >>>    if name in ['running_var']:
            >>>        print(buf.size())

        """
        gen = self._named_members(
            lambda module: module._buffers.items(),
            prefix=prefix, recurse=recurse)
        for elem in gen:
            yield elem

    def children(self) -> Iterator['Module']:
        r"""Returns an iterator over immediate children modules.

        Yields:
            Module: a child module
        """
        for name, module in self.named_children():
            yield module

    def named_children(self) -> Iterator[Tuple[str, 'Module']]:
        r"""Returns an iterator over immediate children modules, yielding both
        the name of the module as well as the module itself.

        Yields:
            (string, Module): Tuple containing a name and child module

        Example::

            >>> for name, module in model.named_children():
            >>>     if name in ['conv4', 'conv5']:
            >>>         print(module)

        """
        memo = set()
        for name, module in self._modules.items():
            if module is not None and module not in memo:
                memo.add(module)
                yield name, module

    def modules(self) -> Iterator['Module']:
        r"""Returns an iterator over all modules in the network.

        Yields:
            Module: a module in the network

        Note:
            Duplicate modules are returned only once. In the following
            example, ``l`` will be returned only once.

        Example::

            >>> l = nn.Linear(2, 2)
            >>> net = nn.Sequential(l, l)
            >>> for idx, m in enumerate(net.modules()):
                    print(idx, '->', m)

            0 -> Sequential(
              (0): Linear(in_features=2, out_features=2, bias=True)
              (1): Linear(in_features=2, out_features=2, bias=True)
            )
            1 -> Linear(in_features=2, out_features=2, bias=True)

        """
        for name, module in self.named_modules():
            yield module

    def named_modules(self, memo: Optional[Set['Module']] = None, prefix: str = ''):
        r"""Returns an iterator over all modules in the network, yielding
        both the name of the module as well as the module itself.

        Yields:
            (string, Module): Tuple of name and module

        Note:
            Duplicate modules are returned only once. In the following
            example, ``l`` will be returned only once.

        Example::

            >>> l = nn.Linear(2, 2)
            >>> net = nn.Sequential(l, l)
            >>> for idx, m in enumerate(net.named_modules()):
                    print(idx, '->', m)

            0 -> ('', Sequential(
              (0): Linear(in_features=2, out_features=2, bias=True)
              (1): Linear(in_features=2, out_features=2, bias=True)
            ))
            1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

        """

        if memo is None:
            memo = set()
        if self not in memo:
            memo.add(self)
            yield prefix, self
            for name, module in self._modules.items():
                if module is None:
                    continue
                submodule_prefix = prefix + ('.' if prefix else '') + name
                for m in module.named_modules(memo, submodule_prefix):
                    yield m

    def train(self: T, mode: bool = True) -> T:
        r"""Sets the module in training mode.

        This has any effect only on certain modules. See documentations of
        particular modules for details of their behaviors in training/evaluation
        mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
        etc.

        Args:
            mode (bool): whether to set training mode (``True``) or evaluation
                         mode (``False``). Default: ``True``.

        Returns:
            Module: self
        """
        self.training = mode
        for module in self.children():
            module.train(mode)
        return self

    def eval(self: T) -> T:
        r"""Sets the module in evaluation mode.

        This has any effect only on certain modules. See documentations of
        particular modules for details of their behaviors in training/evaluation
        mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
        etc.

        This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`.

        Returns:
            Module: self
        """
        return self.train(False)

    def requires_grad_(self: T, requires_grad: bool = True) -> T:
        r"""Change if autograd should record operations on parameters in this
        module.

        This method sets the parameters' :attr:`requires_grad` attributes
        in-place.

        This method is helpful for freezing part of the module for finetuning
        or training parts of a model individually (e.g., GAN training).

        Args:
            requires_grad (bool): whether autograd should record operations on
                                  parameters in this module. Default: ``True``.

        Returns:
            Module: self
        """
        for p in self.parameters():
            p.requires_grad_(requires_grad)
        return self

    def zero_grad(self, set_to_none: bool = False) -> None:
        r"""Sets gradients of all model parameters to zero. See similar function
        under :class:`torch.optim.Optimizer` for more context.

        Arguments:
            set_to_none (bool): instead of setting to zero, set the grads to None.
                See :meth:`torch.optim.Optimizer.zero_grad` for details.
        """
        if getattr(self, '_is_replica', False):
            warnings.warn(
                "Calling .zero_grad() from a module created with nn.DataParallel() has no effect. "
                "The parameters are copied (in a differentiable manner) from the original module. "
                "This means they are not leaf nodes in autograd and so don't accumulate gradients. "
                "If you need gradients in your forward method, consider using autograd.grad instead.")

        for p in self.parameters():
            if p.grad is not None:
                if set_to_none:
                    p.grad = None
                else:
                    if p.grad.grad_fn is not None:
                        p.grad.detach_()
                    else:
                        p.grad.requires_grad_(False)
                    p.grad.zero_()

    def share_memory(self: T) -> T:
        return self._apply(lambda t: t.share_memory_())

    def _get_name(self):
        return self.__class__.__name__

    def extra_repr(self) -> str:
        r"""Set the extra representation of the module

        To print customized extra information, you should re-implement
        this method in your own modules. Both single-line and multi-line
        strings are acceptable.
        """
        return ''

    def __repr__(self):
        # We treat the extra repr like the sub-module, one item per line
        extra_lines = []
        extra_repr = self.extra_repr()
        # empty string will be split into list ['']
        if extra_repr:
            extra_lines = extra_repr.split('\n')
        child_lines = []
        for key, module in self._modules.items():
            mod_str = repr(module)
            mod_str = _addindent(mod_str, 2)
            child_lines.append('(' + key + '): ' + mod_str)
        lines = extra_lines + child_lines

        main_str = self._get_name() + '('
        if lines:
            # simple one-liner info, which most builtin Modules will use
            if len(extra_lines) == 1 and not child_lines:
                main_str += extra_lines[0]
            else:
                main_str += '\n  ' + '\n  '.join(lines) + '\n'

        main_str += ')'
        return main_str

    def __dir__(self):
        module_attrs = dir(self.__class__)
        attrs = list(self.__dict__.keys())
        parameters = list(self._parameters.keys())
        modules = list(self._modules.keys())
        buffers = list(self._buffers.keys())
        keys = module_attrs + attrs + parameters + modules + buffers

        # Eliminate attrs that are not legal Python variable names
        keys = [key for key in keys if not key[0].isdigit()]

        return sorted(keys)

    def _replicate_for_data_parallel(self):
        replica = self.__new__(type(self))
        replica.__dict__ = self.__dict__.copy()

        # replicas do not have parameters themselves, the replicas reference the original
        # module.
        replica._parameters = OrderedDict()
        replica._buffers = replica._buffers.copy()
        replica._modules = replica._modules.copy()
        replica._is_replica = True

        return replica

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