Table of Contents
Shortcuts

# Source code for torch.nn.functional

    """Functional interface"""
    from typing import Callable, List, Optional, Tuple, Union
    import math
    import warnings
    import importlib

    import torch
    from torch import _VF
    from torch import sym_int as _sym_int
    from torch._C import _infer_size, _add_docstr
    from torch._torch_docs import reproducibility_notes, tf32_notes, sparse_support_notes
    # A workaround to support both TorchScript and MyPy:
    from typing import TYPE_CHECKING
    if TYPE_CHECKING:
        from torch.types import _dtype as DType
    else:
        # The JIT doesn't understand Union, nor torch.dtype here
        DType = int

    from .._jit_internal import boolean_dispatch, _overload, BroadcastingList1, BroadcastingList2, BroadcastingList3
    from ..overrides import (
        has_torch_function, has_torch_function_unary, has_torch_function_variadic,
        handle_torch_function)
    from . import _reduction as _Reduction
    from . import grad  # noqa: F401
    from .modules import utils
    from .modules.utils import _single, _pair, _triple, _list_with_default

    Tensor = torch.Tensor

    conv1d = _add_docstr(
        torch.conv1d,
        r"""
    conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

    Applies a 1D convolution over an input signal composed of several input
    planes.

    {tf32_note}

    See :class:`~torch.nn.Conv1d` for details and output shape.

    Note:
        {cudnn_reproducibility_note}

    Note:
        This operator supports complex data types i.e. ``complex32, complex64, complex128``.
    """.format(
            **reproducibility_notes, **tf32_notes
        )
        + r"""

    Args:
        input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
        weight: filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kW)`
        bias: optional bias of shape :math:`(\text{out\_channels})`. Default: ``None``
        stride: the stride of the convolving kernel. Can be a single number or
          a one-element tuple `(sW,)`. Default: 1
        padding: implicit paddings on both sides of the input. Can be a string {'valid', 'same'},
          single number or a one-element tuple `(padW,)`. Default: 0
          ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
          the input so the output has the same shape as the input. However, this mode
          doesn't support any stride values other than 1.

          .. warning::
              For ``padding='same'``, if the ``weight`` is even-length and
              ``dilation`` is odd in any dimension, a full :func:`pad` operation
              may be needed internally. Lowering performance.
        dilation: the spacing between kernel elements. Can be a single number or
          a one-element tuple `(dW,)`. Default: 1
        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by
          the number of groups. Default: 1

    Examples::

        >>> inputs = torch.randn(33, 16, 30)
        >>> filters = torch.randn(20, 16, 5)
        >>> F.conv1d(inputs, filters)
    """,
    )

    conv2d = _add_docstr(
        torch.conv2d,
        r"""
    conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

    Applies a 2D convolution over an input image composed of several input
    planes.

    {tf32_note}

    See :class:`~torch.nn.Conv2d` for details and output shape.

    Note:
        {cudnn_reproducibility_note}

    Note:
        This operator supports complex data types i.e. ``complex32, complex64, complex128``.
    """.format(
            **reproducibility_notes, **tf32_notes
        )
        + r"""

    Args:
        input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
        weight: filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW)`
        bias: optional bias tensor of shape :math:`(\text{out\_channels})`. Default: ``None``
        stride: the stride of the convolving kernel. Can be a single number or a
          tuple `(sH, sW)`. Default: 1
        padding: implicit paddings on both sides of the input. Can be a string {'valid', 'same'},
          single number or a tuple `(padH, padW)`. Default: 0
          ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
          the input so the output has the same shape as the input. However, this mode
          doesn't support any stride values other than 1.

          .. warning::
              For ``padding='same'``, if the ``weight`` is even-length and
              ``dilation`` is odd in any dimension, a full :func:`pad` operation
              may be needed internally. Lowering performance.

        dilation: the spacing between kernel elements. Can be a single number or
          a tuple `(dH, dW)`. Default: 1
        groups: split input into groups, both :math:`\text{in\_channels}` and :math:`\text{out\_channels}`
          should be divisible by the number of groups. Default: 1

    Examples::

        >>> # With square kernels and equal stride
        >>> filters = torch.randn(8, 4, 3, 3)
        >>> inputs = torch.randn(1, 4, 5, 5)
        >>> F.conv2d(inputs, filters, padding=1)
    """,
    )  # noqa: E501

    conv3d = _add_docstr(
        torch.conv3d,
        r"""
    conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor

    Applies a 3D convolution over an input image composed of several input
    planes.

    {tf32_note}

    See :class:`~torch.nn.Conv3d` for details and output shape.

    Note:
        {cudnn_reproducibility_note}

    Note:
        This operator supports complex data types i.e. ``complex32, complex64, complex128``.
    """.format(
            **reproducibility_notes, **tf32_notes
        )
        + r"""

    Args:
        input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iT , iH , iW)`
        weight: filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kT , kH , kW)`
        bias: optional bias tensor of shape :math:`(\text{out\_channels})`. Default: None
        stride: the stride of the convolving kernel. Can be a single number or a
          tuple `(sT, sH, sW)`. Default: 1
        padding: implicit paddings on both sides of the input. Can be a string {'valid', 'same'},
          single number or a tuple `(padT, padH, padW)`. Default: 0
          ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
          the input so the output has the same shape as the input. However, this mode
          doesn't support any stride values other than 1.

          .. warning::
              For ``padding='same'``, if the ``weight`` is even-length and
              ``dilation`` is odd in any dimension, a full :func:`pad` operation
              may be needed internally. Lowering performance.

        dilation: the spacing between kernel elements. Can be a single number or
          a tuple `(dT, dH, dW)`. Default: 1
        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by
          the number of groups. Default: 1

    Examples::

        >>> filters = torch.randn(33, 16, 3, 3, 3)
        >>> inputs = torch.randn(20, 16, 50, 10, 20)
        >>> F.conv3d(inputs, filters)
    """,
    )  # noqa: E501

    conv_transpose1d = _add_docstr(
        torch.conv_transpose1d,
        r"""
    conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor

    Applies a 1D transposed convolution operator over an input signal
    composed of several input planes, sometimes also called "deconvolution".

    {tf32_note}

    See :class:`~torch.nn.ConvTranspose1d` for details and output shape.

    Note:
        {cudnn_reproducibility_note}
    """.format(
            **reproducibility_notes, **tf32_notes
        )
        + r"""

    Args:
        input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
        weight: filters of shape :math:`(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kW)`
        bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None
        stride: the stride of the convolving kernel. Can be a single number or a
          tuple ``(sW,)``. Default: 1
        padding: ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both
          sides of each dimension in the input. Can be a single number or a tuple
          ``(padW,)``. Default: 0
        output_padding: additional size added to one side of each dimension in the
          output shape. Can be a single number or a tuple ``(out_padW)``. Default: 0
        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
          number of groups. Default: 1
        dilation: the spacing between kernel elements. Can be a single number or
          a tuple ``(dW,)``. Default: 1

    Examples::

        >>> inputs = torch.randn(20, 16, 50)
        >>> weights = torch.randn(16, 33, 5)
        >>> F.conv_transpose1d(inputs, weights)
    """,
    )

    conv_transpose2d = _add_docstr(
        torch.conv_transpose2d,
        r"""
    conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor

    Applies a 2D transposed convolution operator over an input image
    composed of several input planes, sometimes also called "deconvolution".

    {tf32_note}

    See :class:`~torch.nn.ConvTranspose2d` for details and output shape.

    Note:
        {cudnn_reproducibility_note}
    """.format(
            **reproducibility_notes, **tf32_notes
        )
        + r"""

    Args:
        input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
        weight: filters of shape :math:`(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kH , kW)`
        bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None
        stride: the stride of the convolving kernel. Can be a single number or a
          tuple ``(sH, sW)``. Default: 1
        padding: ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both
          sides of each dimension in the input. Can be a single number or a tuple
          ``(padH, padW)``. Default: 0
        output_padding: additional size added to one side of each dimension in the
          output shape. Can be a single number or a tuple ``(out_padH, out_padW)``.
          Default: 0
        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
          number of groups. Default: 1
        dilation: the spacing between kernel elements. Can be a single number or
          a tuple ``(dH, dW)``. Default: 1

    Examples::

        >>> # With square kernels and equal stride
        >>> inputs = torch.randn(1, 4, 5, 5)
        >>> weights = torch.randn(4, 8, 3, 3)
        >>> F.conv_transpose2d(inputs, weights, padding=1)
    """,
    )  # noqa: E501

    conv_transpose3d = _add_docstr(
        torch.conv_transpose3d,
        r"""
    conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor

    Applies a 3D transposed convolution operator over an input image
    composed of several input planes, sometimes also called "deconvolution"

    {tf32_note}

    See :class:`~torch.nn.ConvTranspose3d` for details and output shape.

    Note:
        {cudnn_reproducibility_note}
    """.format(
            **reproducibility_notes, **tf32_notes
        )
        + r"""

    Args:
        input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iT , iH , iW)`
        weight: filters of shape :math:`(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kT , kH , kW)`
        bias: optional bias of shape :math:`(\text{out\_channels})`. Default: None
        stride: the stride of the convolving kernel. Can be a single number or a
          tuple ``(sT, sH, sW)``. Default: 1
        padding: ``dilation * (kernel_size - 1) - padding`` zero-padding will be added to both
          sides of each dimension in the input. Can be a single number or a tuple
          ``(padT, padH, padW)``. Default: 0
        output_padding: additional size added to one side of each dimension in the
          output shape. Can be a single number or a tuple
          ``(out_padT, out_padH, out_padW)``. Default: 0
        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
          number of groups. Default: 1
        dilation: the spacing between kernel elements. Can be a single number or
          a tuple `(dT, dH, dW)`. Default: 1

    Examples::

        >>> inputs = torch.randn(20, 16, 50, 10, 20)
        >>> weights = torch.randn(16, 33, 3, 3, 3)
        >>> F.conv_transpose3d(inputs, weights)
    """,
    )  # noqa: E501

    conv_tbc = _add_docstr(
        torch.conv_tbc,
        r"""
    Applies a 1-dimensional sequence convolution over an input sequence.
    Input and output dimensions are (Time, Batch, Channels) - hence TBC.

    Args:
        input: input tensor of shape :math:`(\text{sequence length} \times batch \times \text{in\_channels})`
        weight: filter of shape (:math:`\text{kernel width} \times \text{in\_channels} \times \text{out\_channels}`)
        bias: bias of shape (:math:`\text{out\_channels}`)
        pad: number of timesteps to pad. Default: 0
    """,
    )

    # Pooling
    avg_pool1d = _add_docstr(
        torch.avg_pool1d,
        r"""
    avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor

    Applies a 1D average pooling over an input signal composed of several
    input planes.

    See :class:`~torch.nn.AvgPool1d` for details and output shape.

    Args:
        input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
        kernel_size: the size of the window. Can be a single number or a
          tuple `(kW,)`
        stride: the stride of the window. Can be a single number or a tuple
          `(sW,)`. Default: :attr:`kernel_size`
        padding: implicit zero paddings on both sides of the input. Can be a
          single number or a tuple `(padW,)`. Default: 0
        ceil_mode: when True, will use `ceil` instead of `floor` to compute the
            output shape. Default: ``False``
        count_include_pad: when True, will include the zero-padding in the
            averaging calculation. Default: ``True``

    Examples::

        >>> # pool of square window of size=3, stride=2
        >>> input = torch.tensor([[[1, 2, 3, 4, 5, 6, 7]]], dtype=torch.float32)
        >>> F.avg_pool1d(input, kernel_size=3, stride=2)
        tensor([[[ 2.,  4.,  6.]]])

    """,
    )

    avg_pool2d = _add_docstr(
        torch._C._nn.avg_pool2d,
        r"""
    avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor

    Applies 2D average-pooling operation in :math:`kH \times kW` regions by step size
    :math:`sH \times sW` steps. The number of output features is equal to the number of
    input planes.

    See :class:`~torch.nn.AvgPool2d` for details and output shape.

    Args:
        input: input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
        kernel_size: size of the pooling region. Can be a single number or a
          tuple `(kH, kW)`
        stride: stride of the pooling operation. Can be a single number or a
          tuple `(sH, sW)`. Default: :attr:`kernel_size`
        padding: implicit zero paddings on both sides of the input. Can be a
          single number or a tuple `(padH, padW)`. Default: 0
        ceil_mode: when True, will use `ceil` instead of `floor` in the formula
            to compute the output shape. Default: ``False``
        count_include_pad: when True, will include the zero-padding in the
            averaging calculation. Default: ``True``
        divisor_override: if specified, it will be used as divisor, otherwise
             size of the pooling region will be used. Default: None
    """,
    )

    avg_pool3d = _add_docstr(
        torch._C._nn.avg_pool3d,
        r"""
    avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) -> Tensor

    Applies 3D average-pooling operation in :math:`kT \times kH \times kW` regions by step
    size :math:`sT \times sH \times sW` steps. The number of output features is equal to
    :math:`\lfloor\frac{\text{input planes}}{sT}\rfloor`.

    See :class:`~torch.nn.AvgPool3d` for details and output shape.

    Args:
        input: input tensor :math:`(\text{minibatch} , \text{in\_channels} , iT \times iH , iW)`
        kernel_size: size of the pooling region. Can be a single number or a
          tuple `(kT, kH, kW)`
        stride: stride of the pooling operation. Can be a single number or a
          tuple `(sT, sH, sW)`. Default: :attr:`kernel_size`
        padding: implicit zero paddings on both sides of the input. Can be a
          single number or a tuple `(padT, padH, padW)`, Default: 0
        ceil_mode: when True, will use `ceil` instead of `floor` in the formula
            to compute the output shape
        count_include_pad: when True, will include the zero-padding in the
            averaging calculation
        divisor_override: if specified, it will be used as divisor, otherwise
            size of the pooling region will be used. Default: None
    """,
    )

    def fractional_max_pool2d_with_indices(
        input: Tensor, kernel_size: BroadcastingList2[int],
        output_size: Optional[BroadcastingList2[int]] = None,
        output_ratio: Optional[BroadcastingList2[float]] = None,
        return_indices: bool = False,
        _random_samples: Optional[Tensor] = None
    ) -> Tuple[Tensor, Tensor]:
        r"""
        fractional_max_pool2d(input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None)

        Applies 2D fractional max pooling over an input signal composed of several input planes.

        Fractional MaxPooling is described in detail in the paper `Fractional MaxPooling`_ by Ben Graham

        The max-pooling operation is applied in :math:`kH \times kW` regions by a stochastic
        step size determined by the target output size.
        The number of output features is equal to the number of input planes.

        Args:
            kernel_size: the size of the window to take a max over.
                         Can be a single number :math:`k` (for a square kernel of :math:`k \times k`)
                         or a tuple `(kH, kW)`
            output_size: the target output size of the image of the form :math:`oH \times oW`.
                         Can be a tuple `(oH, oW)` or a single number :math:`oH` for a square image :math:`oH \times oH`
            output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given.
                          This has to be a number or tuple in the range (0, 1)
            return_indices: if ``True``, will return the indices along with the outputs.
                            Useful to pass to :func:`~torch.nn.functional.max_unpool2d`.

        Examples::
            >>> input = torch.randn(20, 16, 50, 32)
            >>> # pool of square window of size=3, and target output size 13x12
            >>> F.fractional_max_pool2d(input, 3, output_size=(13, 12))
            >>> # pool of square window and target output size being half of input image size
            >>> F.fractional_max_pool2d(input, 3, output_ratio=(0.5, 0.5))

        .. _Fractional MaxPooling:
            http://arxiv.org/abs/1412.6071
        """
        if has_torch_function_variadic(input, _random_samples):
            return handle_torch_function(
                fractional_max_pool2d_with_indices,
                (input, _random_samples),
                input,
                kernel_size,
                output_size=output_size,
                output_ratio=output_ratio,
                return_indices=return_indices,
                _random_samples=_random_samples,
            )
        if output_size is None and output_ratio is None:
            raise ValueError("fractional_max_pool2d requires specifying either an output_size or an output_ratio")
        if output_size is None:
            assert output_ratio is not None
            if len(output_ratio) > 2:
                raise ValueError("fractional_max_pool2d requires output_ratio to either be a single Int or tuple of Ints.")
            _output_ratio = _pair(output_ratio)
            output_size = [int(input.size(-2) * _output_ratio[0]), int(input.size(-1) * _output_ratio[1])]

        if _random_samples is None:
            n_batch = 1 if input.dim() == 3 else input.size(0)
            _random_samples = torch.rand(n_batch, input.size(-3), 2, dtype=input.dtype, device=input.device)
        return torch._C._nn.fractional_max_pool2d(input, kernel_size, output_size, _random_samples)

    def _fractional_max_pool2d(
        input: Tensor, kernel_size: BroadcastingList2[int],
        output_size: Optional[BroadcastingList2[int]] = None,
        output_ratio: Optional[BroadcastingList2[float]] = None,
        return_indices: bool = False,
        _random_samples: Optional[Tensor] = None
    ) -> Tensor:
        if has_torch_function_variadic(input, _random_samples):
            return handle_torch_function(
                fractional_max_pool2d,
                (input, _random_samples),
                input,
                kernel_size,
                output_size=output_size,
                output_ratio=output_ratio,
                return_indices=return_indices,
                _random_samples=_random_samples,
            )
        return fractional_max_pool2d_with_indices(
            input, kernel_size, output_size, output_ratio, return_indices, _random_samples
        )[0]

    fractional_max_pool2d = boolean_dispatch(
        arg_name="return_indices",
        arg_index=4,
        default=False,
        if_true=fractional_max_pool2d_with_indices,
        if_false=_fractional_max_pool2d,
        module_name=__name__,
        func_name="fractional_max_pool2d",
    )

    def fractional_max_pool3d_with_indices(
        input: Tensor, kernel_size: BroadcastingList3[int],
        output_size: Optional[BroadcastingList3[int]] = None,
        output_ratio: Optional[BroadcastingList3[float]] = None,
        return_indices: bool = False,
        _random_samples: Optional[Tensor] = None
    ) -> Tuple[Tensor, Tensor]:
        r"""
        fractional_max_pool3d(input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None)

        Applies 3D fractional max pooling over an input signal composed of several input planes.

        Fractional MaxPooling is described in detail in the paper `Fractional MaxPooling`_ by Ben Graham

        The max-pooling operation is applied in :math:`kT \times kH \times kW` regions by a stochastic
        step size determined by the target output size.
        The number of output features is equal to the number of input planes.

        Args:
            kernel_size: the size of the window to take a max over.
                         Can be a single number :math:`k` (for a square kernel of :math:`k \times k \times k`)
                         or a tuple `(kT, kH, kW)`
            output_size: the target output size of the form :math:`oT \times oH \times oW`.
                         Can be a tuple `(oT, oH, oW)` or a single number :math:`oH` for a cubic output
                         :math:`oH \times oH \times oH`
            output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given.
                          This has to be a number or tuple in the range (0, 1)
            return_indices: if ``True``, will return the indices along with the outputs.
                            Useful to pass to :func:`~torch.nn.functional.max_unpool3d`.

        Shape:
            - Input: :math:`(N, C, T_{in}, H_{in}, W_{in})` or :math:`(C, T_{in}, H_{in}, W_{in})`.
            - Output: :math:`(N, C, T_{out}, H_{out}, W_{out})` or :math:`(C, T_{out}, H_{out}, W_{out})`, where
              :math:`(T_{out}, H_{out}, W_{out})=\text{output\_size}` or
              :math:`(T_{out}, H_{out}, W_{out})=\text{output\_ratio} \times (T_{in}, H_{in}, W_{in})`

        Examples::
            >>> input = torch.randn(20, 16, 50, 32, 16)
            >>> # pool of cubic window of size=3, and target output size 13x12x11
            >>> F.fractional_max_pool3d(input, 3, output_size=(13, 12, 11))
            >>> # pool of cubic window and target output size being half of input size
            >>> F.fractional_max_pool3d(input, 3, output_ratio=(0.5, 0.5, 0.5))

        .. _Fractional MaxPooling:
            http://arxiv.org/abs/1412.6071
        """
        if has_torch_function_variadic(input, _random_samples):
            return handle_torch_function(
                fractional_max_pool3d_with_indices,
                (input, _random_samples),
                input,
                kernel_size,
                output_size=output_size,
                output_ratio=output_ratio,
                return_indices=return_indices,
                _random_samples=_random_samples,
            )
        if output_size is None and output_ratio is None:
            raise ValueError("fractional_max_pool3d requires specifying either an output_size or an output_ratio")
        if output_size is None:
            assert output_ratio is not None
            _output_ratio = _triple(output_ratio)
            output_size = [
                int(input.size(-3) * _output_ratio[0]),
                int(input.size(-2) * _output_ratio[1]),
                int(input.size(-1) * _output_ratio[2]),
            ]

        if _random_samples is None:
            n_batch = 1 if input.dim() == 4 else input.size(0)
            _random_samples = torch.rand(n_batch, input.size(-4), 3, dtype=input.dtype, device=input.device)
        return torch._C._nn.fractional_max_pool3d(input, kernel_size, output_size, _random_samples)

    def _fractional_max_pool3d(
        input: Tensor, kernel_size: BroadcastingList3[int],
        output_size: Optional[BroadcastingList3[int]] = None,
        output_ratio: Optional[BroadcastingList3[float]] = None,
        return_indices: bool = False,
        _random_samples: Optional[Tensor] = None
    ) -> Tensor:
        if has_torch_function_variadic(input, _random_samples):
            return handle_torch_function(
                fractional_max_pool3d,
                (input, _random_samples),
                input,
                kernel_size,
                output_size=output_size,
                output_ratio=output_ratio,
                return_indices=return_indices,
                _random_samples=_random_samples,
            )
        return fractional_max_pool3d_with_indices(
            input, kernel_size, output_size, output_ratio, return_indices, _random_samples
        )[0]

    fractional_max_pool3d = boolean_dispatch(
        arg_name="return_indices",
        arg_index=4,
        default=False,
        if_true=fractional_max_pool3d_with_indices,
        if_false=_fractional_max_pool3d,
        module_name=__name__,
        func_name="fractional_max_pool3d",
    )

    def max_pool1d_with_indices(
        input: Tensor, kernel_size: BroadcastingList1[int],
        stride: Optional[BroadcastingList1[int]] = None,
        padding: BroadcastingList1[int] = 0,
        dilation: BroadcastingList1[int] = 1,
        ceil_mode: bool = False,
        return_indices: bool = False
    ) -> Tuple[Tensor, Tensor]:
        r"""
        max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)

        Applies a 1D max pooling over an input signal composed of several input
        planes.

        .. note::
            The order of :attr:`ceil_mode` and :attr:`return_indices` is different from
            what seen in :class:`~torch.nn.MaxPool1d`, and will change in a future release.

        See :class:`~torch.nn.MaxPool1d` for details.

        Args:
            input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`, minibatch dim optional.
            kernel_size: the size of the window. Can be a single number or a
                tuple `(kW,)`
            stride: the stride of the window. Can be a single number or a tuple
                `(sW,)`. Default: :attr:`kernel_size`
            padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.
            dilation: The stride between elements within a sliding window, must be > 0.
            ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This
                       ensures that every element in the input tensor is covered by a sliding window.
            return_indices: If ``True``, will return the argmax along with the max values.
                            Useful for :class:`torch.nn.functional.max_unpool1d` later
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_pool1d_with_indices,
                (input,),
                input,
                kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                ceil_mode=ceil_mode,
                return_indices=return_indices,
            )
        if stride is None:
            stride = torch.jit.annotate(List[int], [])
        return torch.max_pool1d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode)

    def _max_pool1d(
        input: Tensor, kernel_size: BroadcastingList1[int],
        stride: Optional[BroadcastingList1[int]] = None,
        padding: BroadcastingList1[int] = 0,
        dilation: BroadcastingList1[int] = 1,
        ceil_mode: bool = False,
        return_indices: bool = False
    ) -> Tensor:
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_pool1d,
                (input,),
                input,
                kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                ceil_mode=ceil_mode,
                return_indices=return_indices,
            )
        if stride is None:
            stride = torch.jit.annotate(List[int], [])
        return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)

    max_pool1d = boolean_dispatch(
        arg_name="return_indices",
        arg_index=6,
        default=False,
        if_true=max_pool1d_with_indices,
        if_false=_max_pool1d,
        module_name=__name__,
        func_name="max_pool1d",
    )

    def max_pool2d_with_indices(
        input: Tensor, kernel_size: BroadcastingList2[int],
        stride: Optional[BroadcastingList2[int]] = None,
        padding: BroadcastingList2[int] = 0,
        dilation: BroadcastingList2[int] = 1,
        ceil_mode: bool = False,
        return_indices: bool = False
    ) -> Tuple[Tensor, Tensor]:
        r"""
        max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)

        Applies a 2D max pooling over an input signal composed of several input
        planes.

        .. note::
            The order of :attr:`ceil_mode` and :attr:`return_indices` is different from
            what seen in :class:`~torch.nn.MaxPool2d`, and will change in a future release.

        See :class:`~torch.nn.MaxPool2d` for details.

        Args:
            input: input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`, minibatch dim optional.
            kernel_size: size of the pooling region. Can be a single number or a
                tuple `(kH, kW)`
            stride: stride of the pooling operation. Can be a single number or a
                tuple `(sH, sW)`. Default: :attr:`kernel_size`
            padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.
            dilation: The stride between elements within a sliding window, must be > 0.
            ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This
                       ensures that every element in the input tensor is covered by a sliding window.
            return_indices: If ``True``, will return the argmax along with the max values.
                            Useful for :class:`torch.nn.functional.max_unpool2d` later
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_pool2d_with_indices,
                (input,),
                input,
                kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                ceil_mode=ceil_mode,
                return_indices=return_indices,
            )
        if stride is None:
            stride = torch.jit.annotate(List[int], [])
        return torch._C._nn.max_pool2d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode)

    def _max_pool2d(
        input: Tensor, kernel_size: BroadcastingList2[int],
        stride: Optional[BroadcastingList2[int]] = None,
        padding: BroadcastingList2[int] = 0,
        dilation: BroadcastingList2[int] = 1,
        ceil_mode: bool = False,
        return_indices: bool = False
    ) -> Tensor:
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_pool2d,
                (input,),
                input,
                kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                ceil_mode=ceil_mode,
                return_indices=return_indices,
            )
        if stride is None:
            stride = torch.jit.annotate(List[int], [])
        return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)

    max_pool2d = boolean_dispatch(
        arg_name="return_indices",
        arg_index=6,
        default=False,
        if_true=max_pool2d_with_indices,
        if_false=_max_pool2d,
        module_name=__name__,
        func_name="max_pool2d",
    )

    def max_pool3d_with_indices(
        input: Tensor, kernel_size: BroadcastingList3[int],
        stride: Optional[BroadcastingList3[int]] = None,
        padding: BroadcastingList3[int] = 0,
        dilation: BroadcastingList3[int] = 1,
        ceil_mode: bool = False,
        return_indices: bool = False
    ) -> Tuple[Tensor, Tensor]:
        r"""
        max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)

        Applies a 3D max pooling over an input signal composed of several input
        planes.

        .. note::
            The order of :attr:`ceil_mode` and :attr:`return_indices` is different from
            what seen in :class:`~torch.nn.MaxPool3d`, and will change in a future release.

        See :class:`~torch.nn.MaxPool3d` for details.

        Args:
            input: input tensor :math:`(\text{minibatch} , \text{in\_channels} , iD, iH , iW)`, minibatch dim optional.
            kernel_size: size of the pooling region. Can be a single number or a
                         tuple `(kT, kH, kW)`
            stride: stride of the pooling operation. Can be a single number or a
                    tuple `(sT, sH, sW)`. Default: :attr:`kernel_size`
            padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.
            dilation: The stride between elements within a sliding window, must be > 0.
            ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This
                       ensures that every element in the input tensor is covered by a sliding window.
            return_indices: If ``True``, will return the argmax along with the max values.
                            Useful for :class:`torch.nn.functional.max_unpool3d` later
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_pool3d_with_indices,
                (input,),
                input,
                kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                ceil_mode=ceil_mode,
                return_indices=return_indices,
            )
        if stride is None:
            stride = torch.jit.annotate(List[int], [])
        return torch._C._nn.max_pool3d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode)

    def _max_pool3d(
        input: Tensor, kernel_size: BroadcastingList3[int],
        stride: Optional[BroadcastingList3[int]] = None,
        padding: BroadcastingList3[int] = 0,
        dilation: BroadcastingList3[int] = 1,
        ceil_mode: bool = False,
        return_indices: bool = False
    ) -> Tensor:
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_pool3d,
                (input,),
                input,
                kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                ceil_mode=ceil_mode,
                return_indices=return_indices,
            )
        if stride is None:
            stride = torch.jit.annotate(List[int], [])
        return torch.max_pool3d(input, kernel_size, stride, padding, dilation, ceil_mode)

    max_pool3d = boolean_dispatch(
        arg_name="return_indices",
        arg_index=6,
        default=False,
        if_true=max_pool3d_with_indices,
        if_false=_max_pool3d,
        module_name=__name__,
        func_name="max_pool3d",
    )

    def _unpool_output_size(
        input: Tensor, kernel_size: List[int], stride: List[int], padding: List[int], output_size: Optional[List[int]]
    ) -> List[int]:
        input_size = input.size()
        default_size = torch.jit.annotate(List[int], [])
        for d in range(len(kernel_size)):
            default_size.append((input_size[-len(kernel_size) + d] - 1) * stride[d] + kernel_size[d] - 2 * padding[d])
        if output_size is None:
            ret = default_size
        else:
            if len(output_size) == len(kernel_size) + 2:
                output_size = output_size[2:]
            if len(output_size) != len(kernel_size):
                raise ValueError(
                    "output_size should be a sequence containing "
                    f"{len(kernel_size)} or {len(kernel_size) + 2} elements, but it has a length of '{len(output_size)}'"
                )
            for d in range(len(kernel_size)):
                min_size = default_size[d] - stride[d]
                max_size = default_size[d] + stride[d]
                if not (min_size < output_size[d] < max_size):
                    raise ValueError(
                        f'invalid output_size "{output_size}" (dim {d} must be between {min_size} and {max_size})'
                    )

            ret = output_size
        return ret

    def max_unpool1d(
        input: Tensor, indices: Tensor,
        kernel_size: BroadcastingList1[int],
        stride: Optional[BroadcastingList1[int]] = None,
        padding: BroadcastingList1[int] = 0,
        output_size: Optional[BroadcastingList1[int]] = None
    ) -> Tensor:
        r"""Computes a partial inverse of :class:`MaxPool1d`.

        See :class:`~torch.nn.MaxUnpool1d` for details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_unpool1d,
                (input,),
                input,
                indices,
                kernel_size,
                stride=stride,
                padding=padding,
                output_size=output_size,
            )
        kernel_size = _single(kernel_size)
        if stride is not None:
            _stride = _single(stride)
        else:
            _stride = kernel_size
        padding = _single(padding)
        output_size = _unpool_output_size(input, kernel_size, _stride, padding, output_size)
        if isinstance(output_size, list):
            output_size = output_size + [1]
        else:
            output_size = output_size + (1,)
        return torch._C._nn.max_unpool2d(input.unsqueeze(-1), indices.unsqueeze(-1), output_size).squeeze(-1)

    def max_unpool2d(
        input: Tensor, indices: Tensor,
        kernel_size: BroadcastingList2[int],
        stride: Optional[BroadcastingList2[int]] = None,
        padding: BroadcastingList2[int] = 0,
        output_size: Optional[BroadcastingList2[int]] = None
    ) -> Tensor:
        r"""Computes a partial inverse of :class:`MaxPool2d`.

        See :class:`~torch.nn.MaxUnpool2d` for details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_unpool2d,
                (input,),
                input,
                indices,
                kernel_size,
                stride=stride,
                padding=padding,
                output_size=output_size,
            )
        kernel_size = _pair(kernel_size)
        if stride is not None:
            _stride = _pair(stride)
        else:
            _stride = kernel_size
        padding = _pair(padding)
        output_size = _unpool_output_size(input, kernel_size, _stride, padding, output_size)
        return torch._C._nn.max_unpool2d(input, indices, output_size)

    def max_unpool3d(
        input: Tensor, indices: Tensor,
        kernel_size: BroadcastingList3[int],
        stride: Optional[BroadcastingList3[int]] = None,
        padding: BroadcastingList3[int] = 0,
        output_size: Optional[BroadcastingList3[int]] = None
    ) -> Tensor:
        r"""Computes a partial inverse of :class:`MaxPool3d`.

        See :class:`~torch.nn.MaxUnpool3d` for details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                max_unpool3d,
                (input,),
                input,
                indices,
                kernel_size,
                stride=stride,
                padding=padding,
                output_size=output_size,
            )
        kernel_size = _triple(kernel_size)
        if stride is not None:
            _stride = _triple(stride)
        else:
            _stride = kernel_size
        padding = _triple(padding)
        output_size = _unpool_output_size(input, kernel_size, _stride, padding, output_size)
        return torch._C._nn.max_unpool3d(input, indices, output_size, _stride, padding)

    def lp_pool2d(
        input: Tensor, norm_type: Union[int, float],
        kernel_size: BroadcastingList2[int],
        stride: Optional[BroadcastingList2[int]] = None,
        ceil_mode: bool = False
    ) -> Tensor:
        r"""Applies a 2D power-average pooling over an input signal composed of
        several input planes. If the sum of all inputs to the power of `p` is
        zero, the gradient is set to zero as well.

        See :class:`~torch.nn.LPPool2d` for details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                lp_pool2d, (input,), input, norm_type, kernel_size, stride=stride, ceil_mode=ceil_mode
            )
        kw, kh = utils._pair(kernel_size)
        if stride is not None:
            out = avg_pool2d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
        else:
            out = avg_pool2d(input.pow(norm_type), kernel_size, padding=0, ceil_mode=ceil_mode)

        return (torch.sign(out) * relu(torch.abs(out))).mul(kw * kh).pow(1.0 / norm_type)

    def lp_pool1d(
        input: Tensor, norm_type: Union[int, float],
        kernel_size: int,
        stride: Optional[BroadcastingList1[int]] = None,
        ceil_mode: bool = False
    ) -> Tensor:
        r"""Applies a 1D power-average pooling over an input signal composed of
        several input planes. If the sum of all inputs to the power of `p` is
        zero, the gradient is set to zero as well.

        See :class:`~torch.nn.LPPool1d` for details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                lp_pool1d, (input,), input, norm_type, kernel_size, stride=stride, ceil_mode=ceil_mode
            )
        if stride is not None:
            out = avg_pool1d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
        else:
            out = avg_pool1d(input.pow(norm_type), kernel_size, padding=0, ceil_mode=ceil_mode)

        return (torch.sign(out) * relu(torch.abs(out))).mul(kernel_size).pow(1.0 / norm_type)

    def adaptive_max_pool1d_with_indices(
        input: Tensor, output_size: BroadcastingList1[int], return_indices: bool = False
    ) -> Tuple[Tensor, Tensor]:
        r"""
        adaptive_max_pool1d(input, output_size, return_indices=False)

        Applies a 1D adaptive max pooling over an input signal composed of
        several input planes.

        See :class:`~torch.nn.AdaptiveMaxPool1d` for details and output shape.

        Args:
            output_size: the target output size (single integer)
            return_indices: whether to return pooling indices. Default: ``False``
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                adaptive_max_pool1d_with_indices, (input,), input, output_size, return_indices=return_indices
            )
        return torch.adaptive_max_pool1d(input, output_size)

    def _adaptive_max_pool1d(input: Tensor, output_size: BroadcastingList1[int], return_indices: bool = False) -> Tensor:
        if has_torch_function_unary(input):
            return handle_torch_function(
                adaptive_max_pool1d, (input,), input, output_size, return_indices=return_indices
            )
        return adaptive_max_pool1d_with_indices(input, output_size)[0]

    adaptive_max_pool1d = boolean_dispatch(
        arg_name="return_indices",
        arg_index=2,
        default=False,
        if_true=adaptive_max_pool1d_with_indices,
        if_false=_adaptive_max_pool1d,
        module_name=__name__,
        func_name="adaptive_max_pool1d",
    )

    def adaptive_max_pool2d_with_indices(
        input: Tensor, output_size: BroadcastingList2[int],
        return_indices: bool = False
    ) -> Tuple[Tensor, Tensor]:
        r"""
        adaptive_max_pool2d(input, output_size, return_indices=False)

        Applies a 2D adaptive max pooling over an input signal composed of
        several input planes.

        See :class:`~torch.nn.AdaptiveMaxPool2d` for details and output shape.

        Args:
            output_size: the target output size (single integer or
                double-integer tuple)
            return_indices: whether to return pooling indices. Default: ``False``
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                adaptive_max_pool2d_with_indices, (input,), input, output_size, return_indices=return_indices
            )
        output_size = _list_with_default(output_size, input.size())
        return torch._C._nn.adaptive_max_pool2d(input, output_size)

    def _adaptive_max_pool2d(input: Tensor, output_size: BroadcastingList2[int], return_indices: bool = False) -> Tensor:
        if has_torch_function_unary(input):
            return handle_torch_function(
                adaptive_max_pool2d, (input,), input, output_size, return_indices=return_indices
            )
        return adaptive_max_pool2d_with_indices(input, output_size)[0]

    adaptive_max_pool2d = boolean_dispatch(
        arg_name="return_indices",
        arg_index=2,
        default=False,
        if_true=adaptive_max_pool2d_with_indices,
        if_false=_adaptive_max_pool2d,
        module_name=__name__,
        func_name="adaptive_max_pool2d",
    )

    def adaptive_max_pool3d_with_indices(
        input: Tensor, output_size: BroadcastingList3[int],
        return_indices: bool = False
    ) -> Tuple[Tensor, Tensor]:
        r"""
        adaptive_max_pool3d(input, output_size, return_indices=False)

        Applies a 3D adaptive max pooling over an input signal composed of
        several input planes.

        See :class:`~torch.nn.AdaptiveMaxPool3d` for details and output shape.

        Args:
            output_size: the target output size (single integer or
                triple-integer tuple)
            return_indices: whether to return pooling indices. Default: ``False``
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                adaptive_max_pool3d_with_indices, (input,), input, output_size, return_indices=return_indices
            )
        output_size = _list_with_default(output_size, input.size())
        return torch._C._nn.adaptive_max_pool3d(input, output_size)

    def _adaptive_max_pool3d(input: Tensor, output_size: BroadcastingList3[int], return_indices: bool = False) -> Tensor:
        if has_torch_function_unary(input):
            return handle_torch_function(
                adaptive_max_pool3d, (input,), input, output_size, return_indices=return_indices
            )
        return adaptive_max_pool3d_with_indices(input, output_size)[0]

    adaptive_max_pool3d = boolean_dispatch(
        arg_name="return_indices",
        arg_index=2,
        default=False,
        if_true=adaptive_max_pool3d_with_indices,
        if_false=_adaptive_max_pool3d,
        module_name=__name__,
        func_name="adaptive_max_pool3d",
    )

    adaptive_avg_pool1d = _add_docstr(
        torch.adaptive_avg_pool1d,
        r"""
    adaptive_avg_pool1d(input, output_size) -> Tensor

    Applies a 1D adaptive average pooling over an input signal composed of
    several input planes.

    See :class:`~torch.nn.AdaptiveAvgPool1d` for details and output shape.

    Args:
        output_size: the target output size (single integer)
    """,
    )

    def adaptive_avg_pool2d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
        r"""
        Applies a 2D adaptive average pooling over an input signal composed of
        several input planes.

        See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.

        Args:
            output_size: the target output size (single integer or
                double-integer tuple)
        """
        if has_torch_function_unary(input):
            return handle_torch_function(adaptive_avg_pool2d, (input,), input, output_size)
        _output_size = _list_with_default(output_size, input.size())
        return torch._C._nn.adaptive_avg_pool2d(input, _output_size)

    def adaptive_avg_pool3d(input: Tensor, output_size: BroadcastingList3[int]) -> Tensor:
        r"""
        Applies a 3D adaptive average pooling over an input signal composed of
        several input planes.

        See :class:`~torch.nn.AdaptiveAvgPool3d` for details and output shape.

        Args:
            output_size: the target output size (single integer or
                triple-integer tuple)
        """
        if has_torch_function_unary(input):
            return handle_torch_function(adaptive_avg_pool3d, (input,), input, output_size)
        _output_size = _list_with_default(output_size, input.size())
        return torch._C._nn.adaptive_avg_pool3d(input, _output_size)

    # Activation functions
    def dropout(input: Tensor, p: float = 0.5, training: bool = True, inplace: bool = False) -> Tensor:
        r"""
        During training, randomly zeroes some of the elements of the input
        tensor with probability :attr:`p` using samples from a Bernoulli
        distribution.

        See :class:`~torch.nn.Dropout` for details.

        Args:
            p: probability of an element to be zeroed. Default: 0.5
            training: apply dropout if is ``True``. Default: ``True``
            inplace: If set to ``True``, will do this operation in-place. Default: ``False``
        """
        if has_torch_function_unary(input):
            return handle_torch_function(dropout, (input,), input, p=p, training=training, inplace=inplace)
        if p < 0.0 or p > 1.0:
            raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
        return _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)

    def alpha_dropout(input: Tensor, p: float = 0.5, training: bool = False, inplace: bool = False) -> Tensor:
        r"""Applies alpha dropout to the input.

        See :class:`~torch.nn.AlphaDropout` for details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(alpha_dropout, (input,), input, p=p, training=training, inplace=inplace)
        if p < 0.0 or p > 1.0:
            raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
        return _VF.alpha_dropout_(input, p, training) if inplace else _VF.alpha_dropout(input, p, training)

    def dropout1d(input: Tensor, p: float = 0.5, training: bool = True, inplace: bool = False) -> Tensor:
        r"""
        Randomly zero out entire channels (a channel is a 1D feature map,
        e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
        batched input is a 1D tensor :math:`\text{input}[i, j]`) of the input tensor).
        Each channel will be zeroed out independently on every forward call with
        probability :attr:`p` using samples from a Bernoulli distribution.

        See :class:`~torch.nn.Dropout1d` for details.

        Args:
            p: probability of a channel to be zeroed. Default: 0.5
            training: apply dropout if is ``True``. Default: ``True``
            inplace: If set to ``True``, will do this operation in-place. Default: ``False``
        """
        if has_torch_function_unary(input):
            return handle_torch_function(dropout1d, (input,), input, p=p, training=training, inplace=inplace)
        if p < 0.0 or p > 1.0:
            raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
        inp_dim = input.dim()
        if inp_dim not in (2, 3):
            raise RuntimeError(f"dropout1d: Expected 2D or 3D input, but received a {inp_dim}D input. "
                               "Note that dropout1d exists to provide channel-wise dropout on inputs with 1 "
                               "spatial dimension, a channel dimension, and an optional batch dimension "
                               "(i.e. 2D or 3D inputs).")

        is_batched = inp_dim == 3
        if not is_batched:
            input = input.unsqueeze_(0) if inplace else input.unsqueeze(0)

        result = _VF.feature_dropout_(input, p, training) if inplace else _VF.feature_dropout(input, p, training)

        if not is_batched:
            result = result.squeeze_(0) if inplace else result.squeeze(0)

        return result

    def dropout2d(input: Tensor, p: float = 0.5, training: bool = True, inplace: bool = False) -> Tensor:
        r"""
        Randomly zero out entire channels (a channel is a 2D feature map,
        e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
        batched input is a 2D tensor :math:`\text{input}[i, j]`) of the input tensor).
        Each channel will be zeroed out independently on every forward call with
        probability :attr:`p` using samples from a Bernoulli distribution.

        See :class:`~torch.nn.Dropout2d` for details.

        Args:
            p: probability of a channel to be zeroed. Default: 0.5
            training: apply dropout if is ``True``. Default: ``True``
            inplace: If set to ``True``, will do this operation in-place. Default: ``False``
        """
        if has_torch_function_unary(input):
            return handle_torch_function(dropout2d, (input,), input, p=p, training=training, inplace=inplace)
        if p < 0.0 or p > 1.0:
            raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
        inp_dim = input.dim()
        if inp_dim not in (3, 4):
            warn_msg = (f"dropout2d: Received a {inp_dim}-D input to dropout2d, which is deprecated "
                        "and will result in an error in a future release. To retain the behavior "
                        "and silence this warning, please use dropout instead. Note that dropout2d "
                        "exists to provide channel-wise dropout on inputs with 2 spatial dimensions, "
                        "a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).")
            warnings.warn(warn_msg)

        # TODO: Properly support no-batch-dim inputs. For now, these are NOT supported; passing
        # a 3D input will perform dropout1d behavior instead. This was done historically and the
        # behavior is maintained here for now.
        # See https://github.com/pytorch/pytorch/issues/77081
        if inp_dim == 3:
            warnings.warn("dropout2d: Received a 3D input to dropout2d and assuming that channel-wise "
                          "1D dropout behavior is desired - input is interpreted as shape (N, C, L), where C "
                          "is the channel dim. This behavior will change in a future release to interpret the "
                          "input as one without a batch dimension, i.e. shape (C, H, W). To maintain the 1D "
                          "channel-wise dropout behavior, please switch to using dropout1d instead.")

        result = _VF.feature_dropout_(input, p, training) if inplace else _VF.feature_dropout(input, p, training)

        return result

    def dropout3d(input: Tensor, p: float = 0.5, training: bool = True, inplace: bool = False) -> Tensor:
        r"""
        Randomly zero out entire channels (a channel is a 3D feature map,
        e.g., the :math:`j`-th channel of the :math:`i`-th sample in the
        batched input is a 3D tensor :math:`\text{input}[i, j]`) of the input tensor).
        Each channel will be zeroed out independently on every forward call with
        probability :attr:`p` using samples from a Bernoulli distribution.

        See :class:`~torch.nn.Dropout3d` for details.

        Args:
            p: probability of a channel to be zeroed. Default: 0.5
            training: apply dropout if is ``True``. Default: ``True``
            inplace: If set to ``True``, will do this operation in-place. Default: ``False``
        """
        if has_torch_function_unary(input):
            return handle_torch_function(dropout3d, (input,), input, p=p, training=training, inplace=inplace)
        if p < 0.0 or p > 1.0:
            raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
        inp_dim = input.dim()
        if inp_dim not in (4, 5):
            warn_msg = (f"dropout3d: Received a {inp_dim}-D input to dropout3d, which is deprecated "
                        "and will result in an error in a future release. To retain the behavior "
                        "and silence this warning, please use dropout instead. Note that dropout3d "
                        "exists to provide channel-wise dropout on inputs with 3 spatial dimensions, "
                        "a channel dimension, and an optional batch dimension (i.e. 4D or 5D inputs).")
            warnings.warn(warn_msg)

        is_batched = inp_dim == 5
        if not is_batched:
            input = input.unsqueeze_(0) if inplace else input.unsqueeze(0)

        result = _VF.feature_dropout_(input, p, training) if inplace else _VF.feature_dropout(input, p, training)

        if not is_batched:
            result = result.squeeze_(0) if inplace else result.squeeze(0)
        return result

    def feature_alpha_dropout(input: Tensor, p: float = 0.5, training: bool = False, inplace: bool = False) -> Tensor:
        r"""
        Randomly masks out entire channels (a channel is a feature map,
        e.g. the :math:`j`-th channel of the :math:`i`-th sample in the batch input
        is a tensor :math:`\text{input}[i, j]`) of the input tensor). Instead of
        setting activations to zero, as in regular Dropout, the activations are set
        to the negative saturation value of the SELU activation function.

        Each element will be masked independently on every forward call with
        probability :attr:`p` using samples from a Bernoulli distribution.
        The elements to be masked are randomized on every forward call, and scaled
        and shifted to maintain zero mean and unit variance.

        See :class:`~torch.nn.FeatureAlphaDropout` for details.

        Args:
            p: dropout probability of a channel to be zeroed. Default: 0.5
            training: apply dropout if is ``True``. Default: ``True``
            inplace: If set to ``True``, will do this operation in-place. Default: ``False``
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                feature_alpha_dropout, (input,), input, p=p, training=training, inplace=inplace
            )
        if p < 0.0 or p > 1.0:
            raise ValueError(f"dropout probability has to be between 0 and 1, but got {p}")
        return _VF.feature_alpha_dropout_(input, p, training) if inplace else _VF.feature_alpha_dropout(input, p, training)

    def _threshold(input: Tensor, threshold: float, value: float, inplace: bool = False) -> Tensor:
        r"""Thresholds each element of the input Tensor.

        See :class:`~torch.nn.Threshold` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(_threshold, (input,), input, threshold, value, inplace=inplace)
        if inplace:
            result = _VF.threshold_(input, threshold, value)
        else:
            result = _VF.threshold(input, threshold, value)
        return result

    # We define this function as _threshold because it takes an argument
    # named threshold, which clobbers the recursive reference to the
    # function needed for __torch_function__ support
    threshold = _threshold

    threshold_ = _add_docstr(
        _VF.threshold_,
        r"""
    threshold_(input, threshold, value) -> Tensor

    In-place version of :func:`~threshold`.
    """,
    )

    def relu(input: Tensor, inplace: bool = False) -> Tensor:
        r"""relu(input, inplace=False) -> Tensor

        Applies the rectified linear unit function element-wise. See
        :class:`~torch.nn.ReLU` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(relu, (input,), input, inplace=inplace)
        if inplace:
            result = torch.relu_(input)
        else:
            result = torch.relu(input)
        return result

    relu_ = _add_docstr(
        torch.relu_,
        r"""
    relu_(input) -> Tensor

    In-place version of :func:`~relu`.
    """,
    )

    def glu(input: Tensor, dim: int = -1) -> Tensor:
        r"""
        glu(input, dim=-1) -> Tensor

        The gated linear unit. Computes:

        .. math ::
            \text{GLU}(a, b) = a \otimes \sigma(b)

        where `input` is split in half along `dim` to form `a` and `b`, :math:`\sigma`
        is the sigmoid function and :math:`\otimes` is the element-wise product between matrices.

        See `Language Modeling with Gated Convolutional Networks <https://arxiv.org/abs/1612.08083>`_.

        Args:
            input (Tensor): input tensor
            dim (int): dimension on which to split the input. Default: -1
        """
        if has_torch_function_unary(input):
            return handle_torch_function(glu, (input,), input, dim=dim)
        if input.dim() == 0:
            raise RuntimeError("glu does not support scalars because halving size must be even")
        return torch._C._nn.glu(input, dim)

    def hardtanh(input: Tensor, min_val: float = -1., max_val: float = 1., inplace: bool = False) -> Tensor:
        r"""
        hardtanh(input, min_val=-1., max_val=1., inplace=False) -> Tensor

        Applies the HardTanh function element-wise. See :class:`~torch.nn.Hardtanh` for more
        details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(hardtanh, (input,), input, min_val=min_val, max_val=max_val, inplace=inplace)
        if inplace:
            result = torch._C._nn.hardtanh_(input, min_val, max_val)
        else:
            result = torch._C._nn.hardtanh(input, min_val, max_val)
        return result

    hardtanh_ = _add_docstr(
        torch._C._nn.hardtanh_,
        r"""
    hardtanh_(input, min_val=-1., max_val=1.) -> Tensor

    In-place version of :func:`~hardtanh`.
    """,
    )

    def relu6(input: Tensor, inplace: bool = False) -> Tensor:
        r"""relu6(input, inplace=False) -> Tensor

        Applies the element-wise function :math:`\text{ReLU6}(x) = \min(\max(0,x), 6)`.

        See :class:`~torch.nn.ReLU6` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(relu6, (input,), input, inplace=inplace)
        if inplace:
            result = torch._C._nn.relu6_(input)
        else:
            result = torch._C._nn.relu6(input)
        return result

    def elu(input: Tensor, alpha: float = 1.0, inplace: bool = False) -> Tensor:
        r"""Applies the Exponential Linear Unit (ELU) function element-wise.

        See :class:`~torch.nn.ELU` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(elu, (input,), input, alpha=alpha, inplace=inplace)
        if inplace:
            result = torch._C._nn.elu_(input, alpha)
        else:
            result = torch._C._nn.elu(input, alpha)
        return result

    elu_ = _add_docstr(
        torch._C._nn.elu_,
        r"""
    elu_(input, alpha=1.) -> Tensor

    In-place version of :func:`~elu`.
    """,
    )

    def selu(input: Tensor, inplace: bool = False) -> Tensor:
        r"""selu(input, inplace=False) -> Tensor

        Applies element-wise,
        :math:`\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))`,
        with :math:`\alpha=1.6732632423543772848170429916717` and
        :math:`scale=1.0507009873554804934193349852946`.

        See :class:`~torch.nn.SELU` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(selu, (input,), input, inplace=inplace)
        if inplace:
            result = torch.selu_(input)
        else:
            result = torch.selu(input)
        return result

    selu_ = _add_docstr(
        torch.selu_,
        r"""
    selu_(input) -> Tensor

    In-place version of :func:`~selu`.
    """,
    )

    def celu(input: Tensor, alpha: float = 1.0, inplace: bool = False) -> Tensor:
        r"""celu(input, alpha=1., inplace=False) -> Tensor

        Applies element-wise,
        :math:`\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))`.

        See :class:`~torch.nn.CELU` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(celu, (input,), input, alpha=alpha, inplace=inplace)
        if inplace:
            result = torch.celu_(input, alpha)
        else:
            result = torch.celu(input, alpha)
        return result

    celu_ = _add_docstr(
        torch.celu_,
        r"""
    celu_(input, alpha=1.) -> Tensor

    In-place version of :func:`~celu`.
    """,
    )

    def leaky_relu(input: Tensor, negative_slope: float = 0.01, inplace: bool = False) -> Tensor:
        r"""
        leaky_relu(input, negative_slope=0.01, inplace=False) -> Tensor

        Applies element-wise,
        :math:`\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)`

        See :class:`~torch.nn.LeakyReLU` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(leaky_relu, (input,), input, negative_slope=negative_slope, inplace=inplace)
        if inplace:
            result = torch._C._nn.leaky_relu_(input, negative_slope)
        else:
            result = torch._C._nn.leaky_relu(input, negative_slope)
        return result

    leaky_relu_ = _add_docstr(
        torch._C._nn.leaky_relu_,
        r"""
    leaky_relu_(input, negative_slope=0.01) -> Tensor

    In-place version of :func:`~leaky_relu`.
    """,
    )

    prelu = _add_docstr(
        torch.prelu,
        r"""prelu(input, weight) -> Tensor

    Applies element-wise the function
    :math:`\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)` where weight is a
    learnable parameter.

    .. note::
        `weight` is expected to be a scalar or 1-D tensor. If `weight` is 1-D,
        its size must match the number of input channels, determined by
        `input.size(1)` when `input.dim() >= 2`, otherwise 1.
        In the 1-D case, note that when `input` has dim > 2, `weight` can be expanded
        to the shape of `input` in a way that is not possible using normal
        :ref:`broadcasting semantics<broadcasting-semantics>`.

    See :class:`~torch.nn.PReLU` for more details.
    """)

    def rrelu(
        input: Tensor, lower: float = 1.0 / 8, upper: float = 1.0 / 3, training: bool = False, inplace: bool = False
    ) -> Tensor:
        r"""rrelu(input, lower=1./8, upper=1./3, training=False, inplace=False) -> Tensor

        Randomized leaky ReLU.

        See :class:`~torch.nn.RReLU` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                rrelu, (input,), input, lower=lower, upper=upper, training=training, inplace=inplace
            )
        if inplace:
            result = torch.rrelu_(input, lower, upper, training)
        else:
            result = torch.rrelu(input, lower, upper, training)
        return result

    rrelu_ = _add_docstr(
        torch.rrelu_,
        r"""
    rrelu_(input, lower=1./8, upper=1./3, training=False) -> Tensor

    In-place version of :func:`~rrelu`.
    """,
    )

    logsigmoid = _add_docstr(
        torch._C._nn.log_sigmoid,
        r"""
    logsigmoid(input) -> Tensor

    Applies element-wise :math:`\text{LogSigmoid}(x_i) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)`

    See :class:`~torch.nn.LogSigmoid` for more details.
    """,
    )

    gelu = _add_docstr(
        torch._C._nn.gelu,
        r"""
    gelu(input, approximate = 'none') -> Tensor

    When the approximate argument is 'none', it applies element-wise the function
    :math:`\text{GELU}(x) = x * \Phi(x)`

    where :math:`\Phi(x)` is the Cumulative Distribution Function for Gaussian Distribution.

    When the approximate argument is 'tanh', Gelu is estimated with

    .. math::
        \text{GELU}(x) = 0.5 * x * (1 + \text{Tanh}(\sqrt{2 / \pi} * (x + 0.044715 * x^3)))

    See `Gaussian Error Linear Units (GELUs) <https://arxiv.org/abs/1606.08415>`_.
    """)

    hardshrink = _add_docstr(
        torch.hardshrink,
        r"""
    hardshrink(input, lambd=0.5) -> Tensor

    Applies the hard shrinkage function element-wise

    See :class:`~torch.nn.Hardshrink` for more details.
    """)

    def tanhshrink(input):
        r"""tanhshrink(input) -> Tensor

        Applies element-wise, :math:`\text{Tanhshrink}(x) = x - \text{Tanh}(x)`

        See :class:`~torch.nn.Tanhshrink` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(tanhshrink, (input,), input)
        return input - input.tanh()

    def softsign(input):
        r"""softsign(input) -> Tensor

        Applies element-wise, the function :math:`\text{SoftSign}(x) = \frac{x}{1 + |x|}`

        See :class:`~torch.nn.Softsign` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(softsign, (input,), input)
        return input / (input.abs() + 1)

    softplus = _add_docstr(
        torch._C._nn.softplus,
        r"""
    softplus(input, beta=1, threshold=20) -> Tensor

    Applies element-wise, the function :math:`\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))`.

    For numerical stability the implementation reverts to the linear function
    when :math:`input \times \beta > threshold`.

    See :class:`~torch.nn.Softplus` for more details.
    """,
    )

    def _get_softmax_dim(name: str, ndim: int, stacklevel: int) -> int:
        warnings.warn(
            f"Implicit dimension choice for {name} has been deprecated. Change the call to include dim=X as an argument.",
            stacklevel=stacklevel,
        )
        if ndim == 0 or ndim == 1 or ndim == 3:
            ret = 0
        else:
            ret = 1
        return ret

    def softmin(input: Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[DType] = None) -> Tensor:
        r"""Applies a softmin function.

        Note that :math:`\text{Softmin}(x) = \text{Softmax}(-x)`. See softmax definition for mathematical formula.

        See :class:`~torch.nn.Softmin` for more details.

        Args:
            input (Tensor): input
            dim (int): A dimension along which softmin will be computed (so every slice
                along dim will sum to 1).
            dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
              If specified, the input tensor is casted to :attr:`dtype` before the operation
              is performed. This is useful for preventing data type overflows. Default: None.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(softmin, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype)
        if dim is None:
            dim = _get_softmax_dim("softmin", input.dim(), _stacklevel)
        if dtype is None:
            ret = (-input).softmax(dim)
        else:
            ret = (-input).softmax(dim, dtype=dtype)
        return ret

    def softmax(input: Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[DType] = None) -> Tensor:
        r"""Applies a softmax function.

        Softmax is defined as:

        :math:`\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}`

        It is applied to all slices along dim, and will re-scale them so that the elements
        lie in the range `[0, 1]` and sum to 1.

        See :class:`~torch.nn.Softmax` for more details.

        Args:
            input (Tensor): input
            dim (int): A dimension along which softmax will be computed.
            dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
              If specified, the input tensor is casted to :attr:`dtype` before the operation
              is performed. This is useful for preventing data type overflows. Default: None.

        .. note::
            This function doesn't work directly with NLLLoss,
            which expects the Log to be computed between the Softmax and itself.
            Use log_softmax instead (it's faster and has better numerical properties).

        """
        if has_torch_function_unary(input):
            return handle_torch_function(softmax, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype)
        if dim is None:
            dim = _get_softmax_dim("softmax", input.dim(), _stacklevel)
        if dtype is None:
            ret = input.softmax(dim)
        else:
            ret = input.softmax(dim, dtype=dtype)
        return ret

    def gumbel_softmax(logits: Tensor, tau: float = 1, hard: bool = False, eps: float = 1e-10, dim: int = -1) -> Tensor:
        r"""
        Samples from the Gumbel-Softmax distribution (`Link 1`_  `Link 2`_) and optionally discretizes.

        Args:
          logits: `[..., num_features]` unnormalized log probabilities
          tau: non-negative scalar temperature
          hard: if ``True``, the returned samples will be discretized as one-hot vectors,
                but will be differentiated as if it is the soft sample in autograd
          dim (int): A dimension along which softmax will be computed. Default: -1.

        Returns:
          Sampled tensor of same shape as `logits` from the Gumbel-Softmax distribution.
          If ``hard=True``, the returned samples will be one-hot, otherwise they will
          be probability distributions that sum to 1 across `dim`.

        .. note::
          This function is here for legacy reasons, may be removed from nn.Functional in the future.

        .. note::
          The main trick for `hard` is to do  `y_hard - y_soft.detach() + y_soft`

          It achieves two things:
          - makes the output value exactly one-hot
          (since we add then subtract y_soft value)
          - makes the gradient equal to y_soft gradient
          (since we strip all other gradients)

        Examples::
            >>> logits = torch.randn(20, 32)
            >>> # Sample soft categorical using reparametrization trick:
            >>> F.gumbel_softmax(logits, tau=1, hard=False)
            >>> # Sample hard categorical using "Straight-through" trick:
            >>> F.gumbel_softmax(logits, tau=1, hard=True)

        .. _Link 1:
            https://arxiv.org/abs/1611.00712
        .. _Link 2:
            https://arxiv.org/abs/1611.01144
        """
        if has_torch_function_unary(logits):
            return handle_torch_function(gumbel_softmax, (logits,), logits, tau=tau, hard=hard, eps=eps, dim=dim)
        if eps != 1e-10:
            warnings.warn("`eps` parameter is deprecated and has no effect.")

        gumbels = (
            -torch.empty_like(logits, memory_format=torch.legacy_contiguous_format).exponential_().log()
        )  # ~Gumbel(0,1)
        gumbels = (logits + gumbels) / tau  # ~Gumbel(logits,tau)
        y_soft = gumbels.softmax(dim)

        if hard:
            # Straight through.
            index = y_soft.max(dim, keepdim=True)[1]
            y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
            ret = y_hard - y_soft.detach() + y_soft
        else:
            # Reparametrization trick.
            ret = y_soft
        return ret

    def log_softmax(input: Tensor, dim: Optional[int] = None, _stacklevel: int = 3, dtype: Optional[DType] = None) -> Tensor:
        r"""Applies a softmax followed by a logarithm.

        While mathematically equivalent to log(softmax(x)), doing these two
        operations separately is slower and numerically unstable. This function
        uses an alternative formulation to compute the output and gradient correctly.

        See :class:`~torch.nn.LogSoftmax` for more details.

        Args:
            input (Tensor): input
            dim (int): A dimension along which log_softmax will be computed.
            dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
              If specified, the input tensor is cast to :attr:`dtype` before the operation
              is performed. This is useful for preventing data type overflows. Default: None.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(log_softmax, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype)
        if dim is None:
            dim = _get_softmax_dim("log_softmax", input.dim(), _stacklevel)
        if dtype is None:
            ret = input.log_softmax(dim)
        else:
            ret = input.log_softmax(dim, dtype=dtype)
        return ret

    softshrink = _add_docstr(
        torch._C._nn.softshrink,
        r"""
    softshrink(input, lambd=0.5) -> Tensor

    Applies the soft shrinkage function elementwise

    See :class:`~torch.nn.Softshrink` for more details.
    """,
    )

    def tanh(input):
        r"""tanh(input) -> Tensor

        Applies element-wise,
        :math:`\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}`

        See :class:`~torch.nn.Tanh` for more details.
        """
        return input.tanh()

    def sigmoid(input):
        r"""sigmoid(input) -> Tensor

        Applies the element-wise function :math:`\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}`

        See :class:`~torch.nn.Sigmoid` for more details.
        """
        return input.sigmoid()

    def hardsigmoid(input: Tensor, inplace: bool = False) -> Tensor:
        r"""Applies the element-wise function

        .. math::
            \text{Hardsigmoid}(x) = \begin{cases}
                0 & \text{if~} x \le -3, \\
                1 & \text{if~} x \ge +3, \\
                x / 6 + 1 / 2 & \text{otherwise}
            \end{cases}

        Args:
            inplace: If set to ``True``, will do this operation in-place. Default: ``False``

        See :class:`~torch.nn.Hardsigmoid` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(hardsigmoid, (input,), input, inplace=inplace)
        if inplace:
            return torch._C._nn.hardsigmoid_(input)
        return torch._C._nn.hardsigmoid(input)

    linear = _add_docstr(
        torch._C._nn.linear,
        r"""
    linear(input, weight, bias=None) -> Tensor

    Applies a linear transformation to the incoming data: :math:`y = xA^T + b`.

    This operation supports 2-D :attr:`weight` with :ref:`sparse layout<sparse-docs>`

    {sparse_beta_warning}

    This operator supports :ref:`TensorFloat32<tf32_on_ampere>`.

    Shape:

        - Input: :math:`(*, in\_features)` where `*` means any number of
          additional dimensions, including none
        - Weight: :math:`(out\_features, in\_features)` or :math:`(in\_features)`
        - Bias: :math:`(out\_features)` or :math:`()`
        - Output: :math:`(*, out\_features)` or :math:`(*)`, based on the shape of the weight
    """.format(**sparse_support_notes))

    bilinear = _add_docstr(
        torch.bilinear,
        r"""
    bilinear(input1, input2, weight, bias=None) -> Tensor

    Applies a bilinear transformation to the incoming data:
    :math:`y = x_1^T A x_2 + b`

    Shape:

        - input1: :math:`(N, *, H_{in1})` where :math:`H_{in1}=\text{in1\_features}`
          and :math:`*` means any number of additional dimensions.
          All but the last dimension of the inputs should be the same.
        - input2: :math:`(N, *, H_{in2})` where :math:`H_{in2}=\text{in2\_features}`
        - weight: :math:`(\text{out\_features}, \text{in1\_features},
          \text{in2\_features})`
        - bias: :math:`(\text{out\_features})`
        - output: :math:`(N, *, H_{out})` where :math:`H_{out}=\text{out\_features}`
          and all but the last dimension are the same shape as the input.
    """)

    def silu(input: Tensor, inplace: bool = False) -> Tensor:
        r"""Applies the Sigmoid Linear Unit (SiLU) function, element-wise.
        The SiLU function is also known as the swish function.

        .. math::
            \text{silu}(x) = x * \sigma(x), \text{where } \sigma(x) \text{ is the logistic sigmoid.}

        .. note::
            See `Gaussian Error Linear Units (GELUs) <https://arxiv.org/abs/1606.08415>`_
            where the SiLU (Sigmoid Linear Unit) was originally coined, and see
            `Sigmoid-Weighted Linear Units for Neural Network Function Approximation
            in Reinforcement Learning <https://arxiv.org/abs/1702.03118>`_ and `Swish:
            a Self-Gated Activation Function <https://arxiv.org/abs/1710.05941v1>`_
            where the SiLU was experimented with later.

        See :class:`~torch.nn.SiLU` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(silu, (input,), input, inplace=inplace)
        if inplace:
            return torch._C._nn.silu_(input)
        return torch._C._nn.silu(input)

    def mish(input: Tensor, inplace: bool = False) -> Tensor:
        r"""Applies the Mish function, element-wise.
        Mish: A Self Regularized Non-Monotonic Neural Activation Function.

        .. math::
            \text{Mish}(x) = x * \text{Tanh}(\text{Softplus}(x))

        .. note::
            See `Mish: A Self Regularized Non-Monotonic Neural Activation Function <https://arxiv.org/abs/1908.08681>`_

        See :class:`~torch.nn.Mish` for more details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(mish, (input,), input, inplace=inplace)
        if inplace:
            return torch._C._nn.mish_(input)
        return torch._C._nn.mish(input)

    def hardswish(input: Tensor, inplace: bool = False) -> Tensor:
        r"""Applies the hardswish function, element-wise, as described in the paper:

        `Searching for MobileNetV3`_.

        .. math::
            \text{Hardswish}(x) = \begin{cases}
                0 & \text{if~} x \le -3, \\
                x & \text{if~} x \ge +3, \\
                x \cdot (x + 3) /6 & \text{otherwise}
            \end{cases}

        See :class:`~torch.nn.Hardswish` for more details.

        .. _`Searching for MobileNetV3`:
            https://arxiv.org/abs/1905.02244
        """
        if has_torch_function_unary(input):
            return handle_torch_function(hardswish, (input,), input, inplace=inplace)
        if inplace:
            return torch._C._nn.hardswish_(input)
        return torch._C._nn.hardswish(input)

    def _no_grad_embedding_renorm_(weight: Tensor, input: Tensor, max_norm: float, norm_type: float) -> Tuple[Tensor, Tensor]:
        torch.embedding_renorm_(weight.detach(), input, max_norm, norm_type)

    def embedding(
        input: Tensor,
        weight: Tensor,
        padding_idx: Optional[int] = None,
        max_norm: Optional[float] = None,
        norm_type: float = 2.0,
        scale_grad_by_freq: bool = False,
        sparse: bool = False,
    ) -> Tensor:
        r"""A simple lookup table that looks up embeddings in a fixed dictionary and size.

        This module is often used to retrieve word embeddings using indices.
        The input to the module is a list of indices, and the embedding matrix,
        and the output is the corresponding word embeddings.

        See :class:`torch.nn.Embedding` for more details.

        .. note::
            Note that the analytical gradients of this function with respect to
            entries in :attr:`weight` at the row specified by :attr:`padding_idx`
            are expected to differ from the numerical ones.

        .. note::
            Note that `:class:`torch.nn.Embedding` differs from this function in
            that it initializes the row of :attr:`weight` specified by
            :attr:`padding_idx` to all zeros on construction.

        Args:
            input (LongTensor): Tensor containing indices into the embedding matrix
            weight (Tensor): The embedding matrix with number of rows equal to the maximum possible index + 1,
                and number of columns equal to the embedding size
            padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient;
                                         therefore, the embedding vector at :attr:`padding_idx` is not updated during training,
                                         i.e. it remains as a fixed "pad".
            max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
                                        is renormalized to have norm :attr:`max_norm`.
                                        Note: this will modify :attr:`weight` in-place.
            norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
            scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse of frequency of
                                                    the words in the mini-batch. Default ``False``.
            sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` will be a sparse tensor. See Notes under
                                     :class:`torch.nn.Embedding` for more details regarding sparse gradients.

        Shape:
            - Input: LongTensor of arbitrary shape containing the indices to extract
            - Weight: Embedding matrix of floating point type with shape `(V, embedding_dim)`,
              where V = maximum index + 1 and embedding_dim = the embedding size
            - Output: `(*, embedding_dim)`, where `*` is the input shape

        Examples::

            >>> # a batch of 2 samples of 4 indices each
            >>> input = torch.tensor([[1, 2, 4, 5], [4, 3, 2, 9]])
            >>> # an embedding matrix containing 10 tensors of size 3
            >>> embedding_matrix = torch.rand(10, 3)
            >>> # xdoctest: +IGNORE_WANT("non-deterministic")
            >>> F.embedding(input, embedding_matrix)
            tensor([[[ 0.8490,  0.9625,  0.6753],
                     [ 0.9666,  0.7761,  0.6108],
                     [ 0.6246,  0.9751,  0.3618],
                     [ 0.4161,  0.2419,  0.7383]],

                    [[ 0.6246,  0.9751,  0.3618],
                     [ 0.0237,  0.7794,  0.0528],
                     [ 0.9666,  0.7761,  0.6108],
                     [ 0.3385,  0.8612,  0.1867]]])

            >>> # example with padding_idx
            >>> weights = torch.rand(10, 3)
            >>> weights[0, :].zero_()
            >>> embedding_matrix = weights
            >>> input = torch.tensor([[0, 2, 0, 5]])
            >>> F.embedding(input, embedding_matrix, padding_idx=0)
            tensor([[[ 0.0000,  0.0000,  0.0000],
                     [ 0.5609,  0.5384,  0.8720],
                     [ 0.0000,  0.0000,  0.0000],
                     [ 0.6262,  0.2438,  0.7471]]])
        """

        if has_torch_function_variadic(input, weight):
            return handle_torch_function(
                embedding,
                (input, weight),
                input,
                weight,
                padding_idx=padding_idx,
                max_norm=max_norm,
                norm_type=norm_type,
                scale_grad_by_freq=scale_grad_by_freq,
                sparse=sparse,
            )
        if padding_idx is not None:
            if padding_idx > 0:
                assert padding_idx < weight.size(0), "Padding_idx must be within num_embeddings"
            elif padding_idx < 0:
                assert padding_idx >= -weight.size(0), "Padding_idx must be within num_embeddings"
                padding_idx = weight.size(0) + padding_idx
        else:
            padding_idx = -1
        if max_norm is not None:
            # Note [embedding_renorm contiguous]
            # `embedding_renorm_` will call .contiguous() on input anyways, so we
            # call it here and take advantage of the improved locality in the
            # `embedding` call below too.
            input = input.contiguous()
            # Note [embedding_renorm set_grad_enabled]
            # XXX: equivalent to
            # with torch.no_grad():
            #   torch.embedding_renorm_
            # remove once script supports set_grad_enabled
            _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
        return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)

    def embedding_bag(
        input: Tensor,
        weight: Tensor,
        offsets: Optional[Tensor] = None,
        max_norm: Optional[float] = None,
        norm_type: float = 2,
        scale_grad_by_freq: bool = False,
        mode: str = "mean",
        sparse: bool = False,
        per_sample_weights: Optional[Tensor] = None,
        include_last_offset: bool = False,
        padding_idx: Optional[int] = None,
    ) -> Tensor:
        r"""Computes sums, means or maxes of `bags` of embeddings, without instantiating the
        intermediate embeddings.

        See :class:`torch.nn.EmbeddingBag` for more details.

        Note:
            {backward_reproducibility_note}

        Args:
            input (LongTensor): Tensor containing bags of indices into the embedding matrix
            weight (Tensor): The embedding matrix with number of rows equal to the maximum possible index + 1,
                and number of columns equal to the embedding size
            offsets (LongTensor, optional): Only used when :attr:`input` is 1D. :attr:`offsets` determines
                                 the starting index position of each bag (sequence) in :attr:`input`.
            max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
                                        is renormalized to have norm :attr:`max_norm`.
                                        Note: this will modify :attr:`weight` in-place.
            norm_type (float, optional): The ``p`` in the ``p``-norm to compute for the :attr:`max_norm` option.
                                         Default ``2``.
            scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
                                                    the words in the mini-batch. Default ``False``.
                                                    Note: this option is not supported when ``mode="max"``.
            mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
                                     Default: ``"mean"``
            sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` will be a sparse tensor. See Notes under
                                     :class:`torch.nn.Embedding` for more details regarding sparse gradients.
                                     Note: this option is not supported when ``mode="max"``.
            per_sample_weights (Tensor, optional): a tensor of float / double weights, or None
                to indicate all weights should be taken to be 1. If specified, :attr:`per_sample_weights`
                must have exactly the same shape as input and is treated as having the same
                :attr:`offsets`, if those are not None.

            include_last_offset (bool, optional): if ``True``, the size of offsets is equal to the number of bags + 1.
                The last element is the size of the input, or the ending index position of the last bag (sequence).

            padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
                                         gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
                                         during training, i.e. it remains as a fixed "pad". Note that the embedding
                                         vector at :attr:`padding_idx` is excluded from the reduction.

        Shape:
            - :attr:`input` (LongTensor) and :attr:`offsets` (LongTensor, optional)

              - If :attr:`input` is 2D of shape `(B, N)`, it will be treated as ``B`` bags (sequences)
                each of fixed length ``N``, and this will return ``B`` values aggregated in a way
                depending on the :attr:`mode`. :attr:`offsets` is ignored and required to be ``None`` in this case.

              - If :attr:`input` is 1D of shape `(N)`, it will be treated as a concatenation of
                multiple bags (sequences). :attr:`offsets` is required to be a 1D tensor containing
                the starting index positions of each bag in :attr:`input`. Therefore, for :attr:`offsets`
                of shape `(B)`, :attr:`input` will be viewed as having ``B`` bags.
                Empty bags (i.e., having 0-length) will have returned vectors filled by zeros.

            - :attr:`weight` (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`

            - :attr:`per_sample_weights` (Tensor, optional). Has the same shape as :attr:`input`.

            - :attr:`output`: aggregated embedding values of shape `(B, embedding_dim)`

        Examples::

            >>> # an Embedding module containing 10 tensors of size 3
            >>> embedding_matrix = torch.rand(10, 3)
            >>> # a batch of 2 samples of 4 indices each
            >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9])
            >>> offsets = torch.tensor([0, 4])
            >>> # xdoctest: +IGNORE_WANT("non-deterministic")
            >>> F.embedding_bag(input, embedding_matrix, offsets)
            tensor([[ 0.3397,  0.3552,  0.5545],
                    [ 0.5893,  0.4386,  0.5882]])

            >>> # example with padding_idx
            >>> embedding_matrix = torch.rand(10, 3)
            >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9])
            >>> offsets = torch.tensor([0, 4])
            >>> F.embedding_bag(input, embedding_matrix, offsets, padding_idx=2, mode='sum')
            tensor([[ 0.0000,  0.0000,  0.0000],
                    [-0.7082,  3.2145, -2.6251]])
        """
        if has_torch_function_variadic(input, weight, offsets, per_sample_weights):
            return handle_torch_function(
                embedding_bag,
                (input, weight, offsets, per_sample_weights),
                input,
                weight,
                offsets=offsets,
                max_norm=max_norm,
                norm_type=norm_type,
                scale_grad_by_freq=scale_grad_by_freq,
                mode=mode,
                sparse=sparse,
                per_sample_weights=per_sample_weights,
                include_last_offset=include_last_offset,
                padding_idx=padding_idx,
            )
        # Check for backward compatibility.
        # Used to be embedding_bag(weight, input, ...)
        # Now is     embedding_bag(input, weight, ...)
        if weight.dtype == torch.long and input.is_floating_point():
            warnings.warn(
                "Argument order of nn.functional.embedding_bag was changed. "
                "Usage `embedding_bag(weight, input, ...)` is deprecated, "
                "and should now be `embedding_bag(input, weight, ...)`."
            )
            weight, input = input, weight

        if per_sample_weights is not None and input.size() != per_sample_weights.size():
            raise ValueError(
                f"embedding_bag: If per_sample_weights ({per_sample_weights.shape}) is not None, "
                f"then it must have the same shape as the input ({input.shape})"
            )

        if not weight.dim() == 2:
            raise ValueError(
                f"weight has to be a 2D Tensor, but got Tensor of dimension {weight.dim()}"
            )

        if input.dim() == 2:
            if offsets is not None:
                type_str = "<unknown>"
                # TODO: Remove this once script supports type() calls
                if not torch.jit.is_scripting():
                    type_str = str(type(offsets))
                raise ValueError(
                    "if input is 2D, then offsets has to be None"
                    ", as input is treated is a mini-batch of"
                    " fixed length sequences. However, found "
                    f"offsets of type {type_str}"
                )
            offsets = torch.arange(0, input.numel(), input.size(1), dtype=input.dtype, device=input.device)

            input = input.reshape(-1)
            if per_sample_weights is not None:
                per_sample_weights = per_sample_weights.reshape(-1)
        elif input.dim() == 1:
            if offsets is None:
                raise ValueError("offsets has to be a 1D Tensor but got None")
            if offsets.dim() != 1:
                raise ValueError("offsets has to be a 1D Tensor")
        else:
            raise ValueError(f"input has to be 1D or 2D Tensor, but got Tensor of dimension {input.dim()}")
        if mode == "sum":
            mode_enum = 0
        elif mode == "mean":
            mode_enum = 1
        elif mode == "max":
            mode_enum = 2

            if scale_grad_by_freq:
                raise ValueError("max mode does not support scaling the gradient by the frequency")

            if sparse:
                raise ValueError("max mode does not support sparse weights")

        else:
            raise ValueError("mode has to be one of sum, mean or max")

        if max_norm is not None:
            # XXX: equivalent to
            # with torch.no_grad():
            #   torch.nembedding_renorm_
            # remove once script supports set_grad_enabled
            _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)

        if per_sample_weights is not None and mode != "sum":
            raise NotImplementedError(
                "embedding_bag: per_sample_weights was not None. "
                "per_sample_weights is only supported for mode='sum' "
                f"(got mode='{mode}'). Please open a feature request on GitHub."
            )

        ret, _, _, _ = torch.embedding_bag(
            weight, input, offsets, scale_grad_by_freq, mode_enum, sparse, per_sample_weights, include_last_offset, padding_idx
        )
        return ret

    if embedding_bag.__doc__:
        embedding_bag.__doc__ = embedding_bag.__doc__.format(**reproducibility_notes)

    def _verify_batch_size(size: List[int]) -> None:
        # XXX: JIT script does not support the reduce from functools, and mul op is a
        # builtin, which cannot be used as a value to a func yet, so rewrite this size
        # check to a simple equivalent for loop
        #
        # TODO: make use of reduce like below when JIT is ready with the missing features:
        # from operator import mul
        # from functools import reduce
        #
        #   if reduce(mul, size[2:], size[0]) == 1
        size_prods = size[0]
        for i in range(len(size) - 2):
            size_prods *= size[i + 2]
        if size_prods == 1:
            raise ValueError(f"Expected more than 1 value per channel when training, got input size {size}")

    def batch_norm(
        input: Tensor,
        running_mean: Optional[Tensor],
        running_var: Optional[Tensor],
        weight: Optional[Tensor] = None,
        bias: Optional[Tensor] = None,
        training: bool = False,
        momentum: float = 0.1,
        eps: float = 1e-5,
    ) -> Tensor:
        r"""Applies Batch Normalization for each channel across a batch of data.

        See :class:`~torch.nn.BatchNorm1d`, :class:`~torch.nn.BatchNorm2d`,
        :class:`~torch.nn.BatchNorm3d` for details.
        """
        if has_torch_function_variadic(input, running_mean, running_var, weight, bias):
            return handle_torch_function(
                batch_norm,
                (input, running_mean, running_var, weight, bias),
                input,
                running_mean,
                running_var,
                weight=weight,
                bias=bias,
                training=training,
                momentum=momentum,
                eps=eps,
            )
        if training:
            _verify_batch_size(input.size())

        return torch.batch_norm(
            input, weight, bias, running_mean, running_var, training, momentum, eps, torch.backends.cudnn.enabled
        )

    def _verify_spatial_size(size: List[int]) -> None:
        # Verify that there is > 1 spatial element for instance norm calculation.
        size_prods = 1
        for i in range(2, len(size)):
            size_prods *= size[i]
        if size_prods == 1:
            raise ValueError(f"Expected more than 1 spatial element when training, got input size {size}")

    def instance_norm(
        input: Tensor,
        running_mean: Optional[Tensor] = None,
        running_var: Optional[Tensor] = None,
        weight: Optional[Tensor] = None,
        bias: Optional[Tensor] = None,
        use_input_stats: bool = True,
        momentum: float = 0.1,
        eps: float = 1e-5,
    ) -> Tensor:
        r"""Applies Instance Normalization for each channel in each data sample in a
        batch.

        See :class:`~torch.nn.InstanceNorm1d`, :class:`~torch.nn.InstanceNorm2d`,
        :class:`~torch.nn.InstanceNorm3d` for details.
        """
        if has_torch_function_variadic(input, running_mean, running_var, weight, bias):
            return handle_torch_function(
                instance_norm,
                (input, running_mean, running_var, weight, bias),
                input,
                running_mean=running_mean,
                running_var=running_var,
                weight=weight,
                bias=bias,
                use_input_stats=use_input_stats,
                momentum=momentum,
                eps=eps,
            )
        if use_input_stats:
            _verify_spatial_size(input.size())
        return torch.instance_norm(
            input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, torch.backends.cudnn.enabled
        )

    def layer_norm(
        input: Tensor,
        normalized_shape: List[int],
        weight: Optional[Tensor] = None,
        bias: Optional[Tensor] = None,
        eps: float = 1e-5,
    ) -> Tensor:
        r"""Applies Layer Normalization for last certain number of dimensions.

        See :class:`~torch.nn.LayerNorm` for details.
        """
        if has_torch_function_variadic(input, weight, bias):
            return handle_torch_function(
                layer_norm, (input, weight, bias), input, normalized_shape, weight=weight, bias=bias, eps=eps
            )
        return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)

    def group_norm(
        input: Tensor, num_groups: int, weight: Optional[Tensor] = None, bias: Optional[Tensor] = None, eps: float = 1e-5
    ) -> Tensor:
        r"""Applies Group Normalization for last certain number of dimensions.

        See :class:`~torch.nn.GroupNorm` for details.
        """
        if has_torch_function_variadic(input, weight, bias):
            return handle_torch_function(group_norm, (input, weight, bias,), input, num_groups, weight=weight, bias=bias, eps=eps)
        if input.dim() < 2:
            raise RuntimeError(f"Expected at least 2 dimensions for input tensor but received {input.dim()}")
        _verify_batch_size([input.size(0) * input.size(1) // num_groups, num_groups] + list(input.size()[2:]))
        return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled)

    def local_response_norm(input: Tensor, size: int, alpha: float = 1e-4, beta: float = 0.75, k: float = 1.0) -> Tensor:
        r"""Applies local response normalization over an input signal composed of
        several input planes, where channels occupy the second dimension.
        Applies normalization across channels.

        See :class:`~torch.nn.LocalResponseNorm` for details.
        """
        if has_torch_function_unary(input):
            return handle_torch_function(local_response_norm, (input,), input, size, alpha=alpha, beta=beta, k=k)
        dim = input.dim()
        if dim < 3:
            raise ValueError(
                f"Expected 3D or higher dimensionality                          input (got {dim} dimensions)"
            )

        if input.numel() == 0:
            return input

        div = input.mul(input)
        if dim == 3:
            div = div.unsqueeze(1)
            div = pad(div, (0, 0, size // 2, (size - 1) // 2))
            div = avg_pool2d(div, (size, 1), stride=1).squeeze(1)
        else:
            sizes = input.size()
            div = div.view(sizes[0], 1, sizes[1], sizes[2], -1)
            div = pad(div, (0, 0, 0, 0, size // 2, (size - 1) // 2))
            div = avg_pool3d(div, (size, 1, 1), stride=1).squeeze(1)
            div = div.view(sizes)
        div = div.mul(alpha).add(k).pow(beta)
        return input / div

    # loss

    def ctc_loss(
        log_probs: Tensor,
        targets: Tensor,
        input_lengths: Tensor,
        target_lengths: Tensor,
        blank: int = 0,
        reduction: str = "mean",
        zero_infinity: bool = False,
    ) -> Tensor:
        r"""The Connectionist Temporal Classification loss.

        See :class:`~torch.nn.CTCLoss` for details.

        Note:
            {cudnn_reproducibility_note}

        Note:
            {backward_reproducibility_note}

        Args:
            log_probs: :math:`(T, N, C)` or :math:`(T, C)` where `C = number of characters in alphabet including blank`,
                `T = input length`, and `N = batch size`.
                The logarithmized probabilities of the outputs
                (e.g. obtained with :func:`torch.nn.functional.log_softmax`).
            targets: :math:`(N, S)` or `(sum(target_lengths))`.
                Targets cannot be blank. In the second form, the targets are assumed to be concatenated.
            input_lengths: :math:`(N)` or :math:`()`.
                Lengths of the inputs (must each be :math:`\leq T`)
            target_lengths: :math:`(N)` or :math:`()`.
                Lengths of the targets
            blank (int, optional):
                Blank label. Default :math:`0`.
            reduction (str, optional): Specifies the reduction to apply to the output:
                ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
                ``'mean'``: the output losses will be divided by the target lengths and
                then the mean over the batch is taken, ``'sum'``: the output will be
                summed. Default: ``'mean'``
            zero_infinity (bool, optional):
                Whether to zero infinite losses and the associated gradients.
                Default: ``False``
                Infinite losses mainly occur when the inputs are too short
                to be aligned to the targets.

        Example::

            >>> log_probs = torch.randn(50, 16, 20).log_softmax(2).detach().requires_grad_()
            >>> targets = torch.randint(1, 20, (16, 30), dtype=torch.long)
            >>> input_lengths = torch.full((16,), 50, dtype=torch.long)
            >>> target_lengths = torch.randint(10, 30, (16,), dtype=torch.long)
            >>> loss = F.ctc_loss(log_probs, targets, input_lengths, target_lengths)
            >>> loss.backward()
        """
        if has_torch_function_variadic(log_probs, targets, input_lengths, target_lengths):
            return handle_torch_function(
                ctc_loss,
                (log_probs, targets, input_lengths, target_lengths),
                log_probs, targets, input_lengths, target_lengths,
                blank=blank, reduction=reduction, zero_infinity=zero_infinity
            )
        return torch.ctc_loss(
            log_probs, targets, input_lengths, target_lengths, blank, _Reduction.get_enum(reduction), zero_infinity
        )

    if ctc_loss.__doc__:
        ctc_loss.__doc__ = ctc_loss.__doc__.format(**reproducibility_notes)

    def nll_loss(
        input: Tensor,
        target: Tensor,
        weight: Optional[Tensor] = None,
        size_average: Optional[bool] = None,
        ignore_index: int = -100,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""The negative log likelihood loss.

        See :class:`~torch.nn.NLLLoss` for details.

        Args:
            input: :math:`(N, C)` where `C = number of classes` or :math:`(N, C, H, W)`
                in case of 2D Loss, or :math:`(N, C, d_1, d_2, ..., d_K)` where :math:`K \geq 1`
                in the case of K-dimensional loss. `input` is expected to be log-probabilities.
            target: :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`,
                or :math:`(N, d_1, d_2, ..., d_K)` where :math:`K \geq 1` for
                K-dimensional loss.
            weight (Tensor, optional): a manual rescaling weight given to each
                class. If given, has to be a Tensor of size `C`
            size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
                the losses are averaged over each loss element in the batch. Note that for
                some losses, there multiple elements per sample. If the field :attr:`size_average`
                is set to ``False``, the losses are instead summed for each minibatch. Ignored
                when reduce is ``False``. Default: ``True``
            ignore_index (int, optional): Specifies a target value that is ignored
                and does not contribute to the input gradient. When :attr:`size_average` is
                ``True``, the loss is averaged over non-ignored targets. Default: -100
            reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
                losses are averaged or summed over observations for each minibatch depending
                on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
                batch element instead and ignores :attr:`size_average`. Default: ``True``
            reduction (str, optional): Specifies the reduction to apply to the output:
                ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
                ``'mean'``: the sum of the output will be divided by the number of
                elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
                and :attr:`reduce` are in the process of being deprecated, and in the meantime,
                specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

        Example::

            >>> # input is of size N x C = 3 x 5
            >>> input = torch.randn(3, 5, requires_grad=True)
            >>> # each element in target has to have 0 <= value < C
            >>> target = torch.tensor([1, 0, 4])
            >>> output = F.nll_loss(F.log_softmax(input, dim=1), target)
            >>> output.backward()
        """
        if has_torch_function_variadic(input, target, weight):
            return handle_torch_function(
                nll_loss,
                (input, target, weight),
                input,
                target,
                weight=weight,
                size_average=size_average,
                ignore_index=ignore_index,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction = _Reduction.legacy_get_string(size_average, reduce)
        return torch._C._nn.nll_loss_nd(input, target, weight, _Reduction.get_enum(reduction), ignore_index)

    def poisson_nll_loss(
        input: Tensor,
        target: Tensor,
        log_input: bool = True,
        full: bool = False,
        size_average: Optional[bool] = None,
        eps: float = 1e-8,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""Poisson negative log likelihood loss.

        See :class:`~torch.nn.PoissonNLLLoss` for details.

        Args:
            input: expectation of underlying Poisson distribution.
            target: random sample :math:`target \sim \text{Poisson}(input)`.
            log_input: if ``True`` the loss is computed as
                :math:`\exp(\text{input}) - \text{target} * \text{input}`, if ``False`` then loss is
                :math:`\text{input} - \text{target} * \log(\text{input}+\text{eps})`. Default: ``True``
            full: whether to compute full loss, i. e. to add the Stirling
                approximation term. Default: ``False``
                :math:`\text{target} * \log(\text{target}) - \text{target} + 0.5 * \log(2 * \pi * \text{target})`.
            size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
                the losses are averaged over each loss element in the batch. Note that for
                some losses, there multiple elements per sample. If the field :attr:`size_average`
                is set to ``False``, the losses are instead summed for each minibatch. Ignored
                when reduce is ``False``. Default: ``True``
            eps (float, optional): Small value to avoid evaluation of :math:`\log(0)` when
                :attr:`log_input`\ =\ ``False``. Default: 1e-8
            reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
                losses are averaged or summed over observations for each minibatch depending
                on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
                batch element instead and ignores :attr:`size_average`. Default: ``True``
            reduction (str, optional): Specifies the reduction to apply to the output:
                ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
                ``'mean'``: the sum of the output will be divided by the number of
                elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
                and :attr:`reduce` are in the process of being deprecated, and in the meantime,
                specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                poisson_nll_loss,
                (input, target),
                input,
                target,
                log_input=log_input,
                full=full,
                size_average=size_average,
                eps=eps,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction = _Reduction.legacy_get_string(size_average, reduce)
        if reduction != "none" and reduction != "mean" and reduction != "sum":
            ret = input
            raise ValueError(reduction + " is not a valid value for reduction")

        ret = torch.poisson_nll_loss(input, target, log_input, full, eps, _Reduction.get_enum(reduction))
        return ret

    def gaussian_nll_loss(
        input: Tensor,
        target: Tensor,
        var: Tensor,
        full: bool = False,
        eps: float = 1e-6,
        reduction: str = "mean",
    ) -> Tensor:
        r"""Gaussian negative log likelihood loss.

        See :class:`~torch.nn.GaussianNLLLoss` for details.

        Args:
            input: expectation of the Gaussian distribution.
            target: sample from the Gaussian distribution.
            var: tensor of positive variance(s), one for each of the expectations
                in the input (heteroscedastic), or a single one (homoscedastic).
            full (bool, optional): include the constant term in the loss calculation. Default: ``False``.
            eps (float, optional): value added to var, for stability. Default: 1e-6.
            reduction (str, optional): specifies the reduction to apply to the output:
                ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
                ``'mean'``: the output is the average of all batch member losses,
                ``'sum'``: the output is the sum of all batch member losses.
                Default: ``'mean'``.
        """
        if has_torch_function_variadic(input, target, var):
            return handle_torch_function(
                gaussian_nll_loss,
                (input, target, var),
                input,
                target,
                var,
                full=full,
                eps=eps,
                reduction=reduction,
            )

        # Check var size
        # If var.size == input.size, the case is heteroscedastic and no further checks are needed.
        # Otherwise:
        if var.size() != input.size():

            # If var is one dimension short of input, but the sizes match otherwise, then this is a homoscedastic case.
            # e.g. input.size = (10, 2, 3), var.size = (10, 2)
            # -> unsqueeze var so that var.shape = (10, 2, 1)
            # this is done so that broadcasting can happen in the loss calculation
            if input.size()[:-1] == var.size():
                var = torch.unsqueeze(var, -1)

            # This checks if the sizes match up to the final dimension, and the final dimension of var is of size 1.
            # This is also a homoscedastic case.
            # e.g. input.size = (10, 2, 3), var.size = (10, 2, 1)
            elif input.size()[:-1] == var.size()[:-1] and var.size(-1) == 1:  # Heteroscedastic case
                pass

            # If none of the above pass, then the size of var is incorrect.
            else:
                raise ValueError("var is of incorrect size")

        # Check validity of reduction mode
        if reduction != 'none' and reduction != 'mean' and reduction != 'sum':
            raise ValueError(reduction + " is not valid")

        # Entries of var must be non-negative
        if torch.any(var < 0):
            raise ValueError("var has negative entry/entries")

        # Clamp for stability
        var = var.clone()
        with torch.no_grad():
            var.clamp_(min=eps)

        # Calculate the loss
        loss = 0.5 * (torch.log(var) + (input - target)**2 / var)
        if full:
            loss += 0.5 * math.log(2 * math.pi)

        if reduction == 'mean':
            return loss.mean()
        elif reduction == 'sum':
            return loss.sum()
        else:
            return loss

    def kl_div(
        input: Tensor,
        target: Tensor,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
        log_target: bool = False,
    ) -> Tensor:
        r"""The `Kullback-Leibler divergence Loss
        <https://en.wikipedia.org/wiki/Kullback-Leibler_divergence>`__

        See :class:`~torch.nn.KLDivLoss` for details.

        Args:
            input: Tensor of arbitrary shape in log-probabilities.
            target: Tensor of the same shape as input. See :attr:`log_target` for
                the target's interpretation.
            size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
                the losses are averaged over each loss element in the batch. Note that for
                some losses, there multiple elements per sample. If the field :attr:`size_average`
                is set to ``False``, the losses are instead summed for each minibatch. Ignored
                when reduce is ``False``. Default: ``True``
            reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
                losses are averaged or summed over observations for each minibatch depending
                on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
                batch element instead and ignores :attr:`size_average`. Default: ``True``
            reduction (str, optional): Specifies the reduction to apply to the output:
                ``'none'`` | ``'batchmean'`` | ``'sum'`` | ``'mean'``.
                ``'none'``: no reduction will be applied
                ``'batchmean'``: the sum of the output will be divided by the batchsize
                ``'sum'``: the output will be summed
                ``'mean'``: the output will be divided by the number of elements in the output
                Default: ``'mean'``
            log_target (bool): A flag indicating whether ``target`` is passed in the log space.
                It is recommended to pass certain distributions (like ``softmax``)
                in the log space to avoid numerical issues caused by explicit ``log``.
                Default: ``False``

        .. note::
            :attr:`size_average` and :attr:`reduce` are in the process of being deprecated,
            and in the meantime, specifying either of those two args will override :attr:`reduction`.

        .. warning::
            :attr:`reduction` = ``'mean'`` doesn't return the true kl divergence value, please use
            :attr:`reduction` = ``'batchmean'`` which aligns with KL math definition.
        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                kl_div,
                (input, target),
                input,
                target,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
                log_target=log_target,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            if reduction == "mean":
                warnings.warn(
                    "reduction: 'mean' divides the total loss by both the batch size and the support size."
                    "'batchmean' divides only by the batch size, and aligns with the KL div math definition."
                    "'mean' will be changed to behave the same as 'batchmean' in the next major release."
                )

            # special case for batchmean
            if reduction == "batchmean":
                reduction_enum = _Reduction.get_enum("sum")
            else:
                reduction_enum = _Reduction.get_enum(reduction)

        reduced = torch.kl_div(input, target, reduction_enum, log_target=log_target)

        if reduction == "batchmean" and input.dim() != 0:
            reduced = reduced / input.size()[0]

        return reduced

    def cross_entropy(
        input: Tensor,
        target: Tensor,
        weight: Optional[Tensor] = None,
        size_average: Optional[bool] = None,
        ignore_index: int = -100,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
        label_smoothing: float = 0.0,
    ) -> Tensor:
        r"""This criterion computes the cross entropy loss between input logits and target.

        See :class:`~torch.nn.CrossEntropyLoss` for details.

        Args:
            input (Tensor) : Predicted unnormalized logits;
                see Shape section below for supported shapes.
            target (Tensor) : Ground truth class indices or class probabilities;
                see Shape section below for supported shapes.
            weight (Tensor, optional): a manual rescaling weight given to each
                class. If given, has to be a Tensor of size `C`
            size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
                the losses are averaged over each loss element in the batch. Note that for
                some losses, there multiple elements per sample. If the field :attr:`size_average`
                is set to ``False``, the losses are instead summed for each minibatch. Ignored
                when reduce is ``False``. Default: ``True``
            ignore_index (int, optional): Specifies a target value that is ignored
                and does not contribute to the input gradient. When :attr:`size_average` is
                ``True``, the loss is averaged over non-ignored targets. Note that
                :attr:`ignore_index` is only applicable when the target contains class indices.
                Default: -100
            reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
                losses are averaged or summed over observations for each minibatch depending
                on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
                batch element instead and ignores :attr:`size_average`. Default: ``True``
            reduction (str, optional): Specifies the reduction to apply to the output:
                ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
                ``'mean'``: the sum of the output will be divided by the number of
                elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
                and :attr:`reduce` are in the process of being deprecated, and in the meantime,
                specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
            label_smoothing (float, optional): A float in [0.0, 1.0]. Specifies the amount
                of smoothing when computing the loss, where 0.0 means no smoothing. The targets
                become a mixture of the original ground truth and a uniform distribution as described in
                `Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.

        Shape:
            - Input: Shape :math:`(C)`, :math:`(N, C)` or :math:`(N, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1`
              in the case of `K`-dimensional loss.
            - Target: If containing class indices, shape :math:`()`, :math:`(N)` or :math:`(N, d_1, d_2, ..., d_K)` with
              :math:`K \geq 1` in the case of K-dimensional loss where each value should be between :math:`[0, C)`.
              If containing class probabilities, same shape as the input and each value should be between :math:`[0, 1]`.

            where:

            .. math::
                \begin{aligned}
                    C ={} & \text{number of classes} \\
                    N ={} & \text{batch size} \\
                \end{aligned}

        Examples::

            >>> # Example of target with class indices
            >>> input = torch.randn(3, 5, requires_grad=True)
            >>> target = torch.randint(5, (3,), dtype=torch.int64)
            >>> loss = F.cross_entropy(input, target)
            >>> loss.backward()
            >>>
            >>> # Example of target with class probabilities
            >>> input = torch.randn(3, 5, requires_grad=True)
            >>> target = torch.randn(3, 5).softmax(dim=1)
            >>> loss = F.cross_entropy(input, target)
            >>> loss.backward()
        """
        if has_torch_function_variadic(input, target, weight):
            return handle_torch_function(
                cross_entropy,
                (input, target, weight),
                input,
                target,
                weight=weight,
                size_average=size_average,
                ignore_index=ignore_index,
                reduce=reduce,
                reduction=reduction,
                label_smoothing=label_smoothing,
            )
        if size_average is not None or reduce is not None:
            reduction = _Reduction.legacy_get_string(size_average, reduce)
        return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)

    def binary_cross_entropy(
        input: Tensor,
        target: Tensor,
        weight: Optional[Tensor] = None,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""Function that measures the Binary Cross Entropy between the target and input
        probabilities.

        See :class:`~torch.nn.BCELoss` for details.

        Args:
            input: Tensor of arbitrary shape as probabilities.
            target: Tensor of the same shape as input with values between 0 and 1.
            weight (Tensor, optional): a manual rescaling weight
                    if provided it's repeated to match input tensor shape
            size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
                the losses are averaged over each loss element in the batch. Note that for
                some losses, there multiple elements per sample. If the field :attr:`size_average`
                is set to ``False``, the losses are instead summed for each minibatch. Ignored
                when reduce is ``False``. Default: ``True``
            reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
                losses are averaged or summed over observations for each minibatch depending
                on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
                batch element instead and ignores :attr:`size_average`. Default: ``True``
            reduction (str, optional): Specifies the reduction to apply to the output:
                ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
                ``'mean'``: the sum of the output will be divided by the number of
                elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
                and :attr:`reduce` are in the process of being deprecated, and in the meantime,
                specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``

        Examples::

            >>> input = torch.randn(3, 2, requires_grad=True)
            >>> target = torch.rand(3, 2, requires_grad=False)
            >>> loss = F.binary_cross_entropy(torch.sigmoid(input), target)
            >>> loss.backward()
        """
        if has_torch_function_variadic(input, target, weight):
            return handle_torch_function(
                binary_cross_entropy,
                (input, target, weight),
                input,
                target,
                weight=weight,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)
        if target.size() != input.size():
            raise ValueError(
                "Using a target size ({}) that is different to the input size ({}) is deprecated. "
                "Please ensure they have the same size.".format(target.size(), input.size())
            )

        if weight is not None:
            new_size = _infer_size(target.size(), weight.size())
            weight = weight.expand(new_size)

        return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum)

    def binary_cross_entropy_with_logits(
        input: Tensor,
        target: Tensor,
        weight: Optional[Tensor] = None,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
        pos_weight: Optional[Tensor] = None,
    ) -> Tensor:
        r"""Function that measures Binary Cross Entropy between target and input
        logits.

        See :class:`~torch.nn.BCEWithLogitsLoss` for details.

        Args:
            input: Tensor of arbitrary shape as unnormalized scores (often referred to as logits).
            target: Tensor of the same shape as input with values between 0 and 1
            weight (Tensor, optional): a manual rescaling weight
                if provided it's repeated to match input tensor shape
            size_average (bool, optional): Deprecated (see :attr:`reduction`). By default,
                the losses are averaged over each loss element in the batch. Note that for
                some losses, there multiple elements per sample. If the field :attr:`size_average`
                is set to ``False``, the losses are instead summed for each minibatch. Ignored
                when reduce is ``False``. Default: ``True``
            reduce (bool, optional): Deprecated (see :attr:`reduction`). By default, the
                losses are averaged or summed over observations for each minibatch depending
                on :attr:`size_average`. When :attr:`reduce` is ``False``, returns a loss per
                batch element instead and ignores :attr:`size_average`. Default: ``True``
            reduction (str, optional): Specifies the reduction to apply to the output:
                ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
                ``'mean'``: the sum of the output will be divided by the number of
                elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average`
                and :attr:`reduce` are in the process of being deprecated, and in the meantime,
                specifying either of those two args will override :attr:`reduction`. Default: ``'mean'``
            pos_weight (Tensor, optional): a weight of positive examples to be broadcasted with target.
                Must be a tensor with equal size along the class dimension to the number of classes.
                Pay close attention to PyTorch's broadcasting semantics in order to achieve the desired
                operations. For a target of size [B, C, H, W] (where B is batch size) pos_weight of
                size [B, C, H, W] will apply different pos_weights to each element of the batch or
                [C, H, W] the same pos_weights across the batch. To apply the same positive weight
                along all spacial dimensions for a 2D multi-class target [C, H, W] use: [C, 1, 1].
                Default: ``None``

        Examples::

             >>> input = torch.randn(3, requires_grad=True)
             >>> target = torch.empty(3).random_(2)
             >>> loss = F.binary_cross_entropy_with_logits(input, target)
             >>> loss.backward()
        """
        if has_torch_function_variadic(input, target, weight, pos_weight):
            return handle_torch_function(
                binary_cross_entropy_with_logits,
                (input, target, weight, pos_weight),
                input,
                target,
                weight=weight,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
                pos_weight=pos_weight,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)

        if not (target.size() == input.size()):
            raise ValueError(f"Target size ({target.size()}) must be the same as input size ({input.size()})")

        return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)

    def smooth_l1_loss(
        input: Tensor,
        target: Tensor,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
        beta: float = 1.0,
    ) -> Tensor:
        r"""Function that uses a squared term if the absolute
        element-wise error falls below beta and an L1 term otherwise.

        See :class:`~torch.nn.SmoothL1Loss` for details.
        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                smooth_l1_loss,
                (input, target),
                input,
                target,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
                beta=beta,
            )
        if not (target.size() == input.size()):
            warnings.warn(
                f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
                "This will likely lead to incorrect results due to broadcasting. "
                "Please ensure they have the same size.",
                stacklevel=2,
            )
        if size_average is not None or reduce is not None:
            reduction = _Reduction.legacy_get_string(size_average, reduce)

        expanded_input, expanded_target = torch.broadcast_tensors(input, target)

        if beta == 0.0:
            return torch._C._nn.l1_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))
        else:
            return torch._C._nn.smooth_l1_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction), beta)

    def huber_loss(
        input: Tensor,
        target: Tensor,
        reduction: str = 'mean',
        delta: float = 1.0,
    ) -> Tensor:
        r"""Function that uses a squared term if the absolute
        element-wise error falls below delta and a delta-scaled L1 term otherwise.

        See :class:`~torch.nn.HuberLoss` for details.
        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                huber_loss,
                (input, target),
                input,
                target,
                reduction=reduction,
                delta=delta,
            )
        if not (target.size() == input.size()):
            warnings.warn(f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
                          "This will likely lead to incorrect results due to broadcasting. "
                          "Please ensure they have the same size.",
                          stacklevel=2)

        expanded_input, expanded_target = torch.broadcast_tensors(input, target)
        return torch._C._nn.huber_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction), delta)

    def l1_loss(
        input: Tensor,
        target: Tensor,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""l1_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor

        Function that takes the mean element-wise absolute value difference.

        See :class:`~torch.nn.L1Loss` for details.
        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                l1_loss, (input, target), input, target, size_average=size_average, reduce=reduce, reduction=reduction
            )
        if not (target.size() == input.size()):
            warnings.warn(
                f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
                "This will likely lead to incorrect results due to broadcasting. "
                "Please ensure they have the same size.",
                stacklevel=2,
            )
        if size_average is not None or reduce is not None:
            reduction = _Reduction.legacy_get_string(size_average, reduce)

        expanded_input, expanded_target = torch.broadcast_tensors(input, target)
        return torch._C._nn.l1_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))

    def mse_loss(
        input: Tensor,
        target: Tensor,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""mse_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor

        Measures the element-wise mean squared error.

        See :class:`~torch.nn.MSELoss` for details.
        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                mse_loss, (input, target), input, target, size_average=size_average, reduce=reduce, reduction=reduction
            )
        if not (target.size() == input.size()):
            warnings.warn(
                f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
                "This will likely lead to incorrect results due to broadcasting. "
                "Please ensure they have the same size.",
                stacklevel=2,
            )
        if size_average is not None or reduce is not None:
            reduction = _Reduction.legacy_get_string(size_average, reduce)

        expanded_input, expanded_target = torch.broadcast_tensors(input, target)
        return torch._C._nn.mse_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))

    def margin_ranking_loss(
        input1: Tensor,
        input2: Tensor,
        target: Tensor,
        margin: float = 0,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""margin_ranking_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') -> Tensor

        See :class:`~torch.nn.MarginRankingLoss` for details.
        """
        if has_torch_function_variadic(input1, input2, target):
            return handle_torch_function(
                margin_ranking_loss,
                (input1, input2, target),
                input1,
                input2,
                target,
                margin=margin,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)
        if (input1.dim() != input2.dim() or input1.dim() != target.dim()):
            raise RuntimeError(
                f"margin_ranking_loss : All input tensors should have same dimension but got sizes: "
                f"input1: {input1.size()}, input2: {input2.size()}, target: {target.size()} "
            )
        return torch.margin_ranking_loss(input1, input2, target, margin, reduction_enum)

    def hinge_embedding_loss(
        input: Tensor,
        target: Tensor,
        margin: float = 1.0,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""hinge_embedding_loss(input, target, margin=1.0, size_average=None, reduce=None, reduction='mean') -> Tensor

        See :class:`~torch.nn.HingeEmbeddingLoss` for details.
        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                hinge_embedding_loss,
                (input, target),
                input,
                target,
                margin=margin,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)
        return torch.hinge_embedding_loss(input, target, margin, reduction_enum)

    def multilabel_margin_loss(
        input: Tensor,
        target: Tensor,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""multilabel_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor

        See :class:`~torch.nn.MultiLabelMarginLoss` for details.
        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                multilabel_margin_loss,
                (input, target),
                input,
                target,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)
        return torch._C._nn.multilabel_margin_loss(input, target, reduction_enum)

    def soft_margin_loss(
        input: Tensor,
        target: Tensor,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""soft_margin_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor

        See :class:`~torch.nn.SoftMarginLoss` for details.
        """
        if has_torch_function_variadic(input, target):
            return handle_torch_function(
                soft_margin_loss, (input, target), input, target, size_average=size_average, reduce=reduce, reduction=reduction
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)
        return torch._C._nn.soft_margin_loss(input, target, reduction_enum)

    def multilabel_soft_margin_loss(
        input: Tensor,
        target: Tensor,
        weight: Optional[Tensor] = None,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""multilabel_soft_margin_loss(input, target, weight=None, size_average=None, reduce=None, reduction='mean') -> Tensor

        See :class:`~torch.nn.MultiLabelSoftMarginLoss` for details.
        """
        if has_torch_function_variadic(input, target, weight):
            return handle_torch_function(
                multilabel_soft_margin_loss,
                (input, target, weight),
                input,
                target,
                weight=weight,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction = _Reduction.legacy_get_string(size_average, reduce)

        loss = -(target * logsigmoid(input) + (1 - target) * logsigmoid(-input))

        if weight is not None:
            loss = loss * weight

        class_dim = input.dim() - 1
        C = input.size(class_dim)
        loss = loss.sum(dim=class_dim) / C  # only return N loss values

        if reduction == "none":
            ret = loss
        elif reduction == "mean":
            ret = loss.mean()
        elif reduction == "sum":
            ret = loss.sum()
        else:
            ret = input
            raise ValueError(reduction + " is not valid")
        return ret

    def cosine_embedding_loss(
        input1: Tensor,
        input2: Tensor,
        target: Tensor,
        margin: float = 0,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""cosine_embedding_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') -> Tensor

        See :class:`~torch.nn.CosineEmbeddingLoss` for details.
        """
        if has_torch_function_variadic(input1, input2, target):
            return handle_torch_function(
                cosine_embedding_loss,
                (input1, input2, target),
                input1,
                input2,
                target,
                margin=margin,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)
        return torch.cosine_embedding_loss(input1, input2, target, margin, reduction_enum)

    def multi_margin_loss(
        input: Tensor,
        target: Tensor,
        p: int = 1,
        margin: float = 1.0,
        weight: Optional[Tensor] = None,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""multi_margin_loss(input, target, p=1, margin=1, weight=None, size_average=None, reduce=None, reduction='mean') -> Tensor

        See :class:`~torch.nn.MultiMarginLoss` for details.
        """
        if has_torch_function_variadic(input, target, weight):
            return handle_torch_function(
                multi_margin_loss,
                (input, target, weight),
                input,
                target,
                p=p,
                margin=margin,
                weight=weight,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)
        if p != 1 and p != 2:
            raise ValueError("only p == 1 and p == 2 supported")
        if weight is not None:
            if weight.dim() != 1:
                raise ValueError("weight must be one-dimensional")

        return torch._C._nn.multi_margin_loss(input, target, p, margin, weight, reduction_enum)

    pixel_shuffle = _add_docstr(
        torch.pixel_shuffle,
        r"""
    pixel_shuffle(input, upscale_factor) -> Tensor

    Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)` to a
    tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is the :attr:`upscale_factor`.

    See :class:`~torch.nn.PixelShuffle` for details.

    Args:
        input (Tensor): the input tensor
        upscale_factor (int): factor to increase spatial resolution by

    Examples::

        >>> input = torch.randn(1, 9, 4, 4)
        >>> output = torch.nn.functional.pixel_shuffle(input, 3)
        >>> print(output.size())
        torch.Size([1, 1, 12, 12])
    """,
    )

    pixel_unshuffle = _add_docstr(
        torch.pixel_unshuffle,
        r"""
    pixel_unshuffle(input, downscale_factor) -> Tensor

    Reverses the :class:`~torch.nn.PixelShuffle` operation by rearranging elements in a
    tensor of shape :math:`(*, C, H \times r, W \times r)` to a tensor of shape
    :math:`(*, C \times r^2, H, W)`, where r is the :attr:`downscale_factor`.

    See :class:`~torch.nn.PixelUnshuffle` for details.

    Args:
        input (Tensor): the input tensor
        downscale_factor (int): factor to increase spatial resolution by

    Examples::

        >>> input = torch.randn(1, 1, 12, 12)
        >>> output = torch.nn.functional.pixel_unshuffle(input, 3)
        >>> print(output.size())
        torch.Size([1, 9, 4, 4])
    """,
    )

    channel_shuffle = _add_docstr(
        torch.channel_shuffle,
        r"""
    channel_shuffle(input, groups) -> Tensor

    Divide the channels in a tensor of shape :math:`(*, C , H, W)`
    into g groups and rearrange them as :math:`(*, C \frac g, g, H, W)`,
    while keeping the original tensor shape.

    See :class:`~torch.nn.ChannelShuffle` for details.

    Args:
        input (Tensor): the input tensor
        groups (int): number of groups to divide channels in and rearrange.

    Examples::

        >>> input = torch.randn(1, 4, 2, 2)
        >>> print(input)
        [[[[1, 2],
           [3, 4]],
          [[5, 6],
           [7, 8]],
          [[9, 10],
           [11, 12]],
          [[13, 14],
           [15, 16]],
         ]]
        >>> output = torch.nn.functional.channel_shuffle(input, 2)
        >>> print(output)
        [[[[1, 2],
           [3, 4]],
          [[9, 10],
           [11, 12]],
          [[5, 6],
           [7, 8]],
          [[13, 14],
           [15, 16]],
         ]]
    """,
    )

    native_channel_shuffle = _add_docstr(
        torch.native_channel_shuffle,
        r"""
    native_channel_shuffle(input, groups) -> Tensor

    Native kernel level implementation of the `channel_shuffle`.
    This function might become private in future releases, use with caution.

    Divide the channels in a tensor of shape :math:`(*, C , H, W)`
    into g groups and rearrange them as :math:`(*, C \frac g, g, H, W)`,
    while keeping the original tensor shape.

    See :class:`~torch.nn.ChannelShuffle` for details.

    Args:
        input (Tensor): the input tensor
        groups (int): number of groups to divide channels in and rearrange.

    Examples::

        >>> input = torch.randn(1, 4, 2, 2)
        >>> print(input)
        [[[[1, 2],
           [3, 4]],
          [[5, 6],
           [7, 8]],
          [[9, 10],
           [11, 12]],
          [[13, 14],
           [15, 16]],
         ]]
        >>> output = torch.nn.functional.native_channel_shuffle(input, 2)
        >>> print(output)
        [[[[1, 2],
           [3, 4]],
          [[9, 10],
           [11, 12]],
          [[5, 6],
           [7, 8]],
          [[13, 14],
           [15, 16]],
         ]]
    """,
    )

    @_overload  # noqa: F811
    def upsample(input: Tensor, size: Optional[int] = None, scale_factor: Optional[float] = None, mode: str = "nearest", align_corners: Optional[bool] = None) -> Tensor:  # noqa: F811,B950
        pass

    @_overload  # noqa: F811
    def upsample(input: Tensor, size: Optional[List[int]] = None, scale_factor: Optional[float] = None, mode: str = "nearest", align_corners: Optional[bool] = None) -> Tensor:  # noqa: F811,B950
        pass

    def upsample(input, size=None, scale_factor=None, mode="nearest", align_corners=None):  # noqa: F811
        r"""Upsamples the input to either the given :attr:`size` or the given
        :attr:`scale_factor`

        .. warning::
            This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
            This is equivalent with ``nn.functional.interpolate(...)``.

        Note:
            {backward_reproducibility_note}

        The algorithm used for upsampling is determined by :attr:`mode`.

        Currently temporal, spatial and volumetric upsampling are supported, i.e.
        expected inputs are 3-D, 4-D or 5-D in shape.

        The input dimensions are interpreted in the form:
        `mini-batch x channels x [optional depth] x [optional height] x width`.

        The modes available for upsampling are: `nearest`, `linear` (3D-only),
        `bilinear`, `bicubic` (4D-only), `trilinear` (5D-only)

        Args:
            input (Tensor): the input tensor
            size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
                output spatial size.
            scale_factor (float or Tuple[float]): multiplier for spatial size. Has to match input size if it is a tuple.
            mode (str): algorithm used for upsampling:
                ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |
                ``'trilinear'``. Default: ``'nearest'``
            align_corners (bool, optional): Geometrically, we consider the pixels of the
                input and output as squares rather than points.
                If set to ``True``, the input and output tensors are aligned by the
                center points of their corner pixels, preserving the values at the corner pixels.
                If set to ``False``, the input and output tensors are aligned by the corner
                points of their corner pixels, and the interpolation uses edge value padding
                for out-of-boundary values, making this operation *independent* of input size
                when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`
                is ``'linear'``, ``'bilinear'``, ``'bicubic'`` or ``'trilinear'``.
                Default: ``False``

        .. note::
            With ``mode='bicubic'``, it's possible to cause overshoot, in other words it can produce
            negative values or values greater than 255 for images.
            Explicitly call ``result.clamp(min=0, max=255)`` if you want to reduce the overshoot
            when displaying the image.

        .. warning::
            With ``align_corners = True``, the linearly interpolating modes
            (`linear`, `bilinear`, and `trilinear`) don't proportionally align the
            output and input pixels, and thus the output values can depend on the
            input size. This was the default behavior for these modes up to version
            0.3.1. Since then, the default behavior is ``align_corners = False``.
            See :class:`~torch.nn.Upsample` for concrete examples on how this
            affects the outputs.

        """
        warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
        return interpolate(input, size, scale_factor, mode, align_corners)

    if upsample.__doc__:
        upsample.__doc__ = upsample.__doc__.format(**reproducibility_notes)

    def _is_integer(x) -> bool:
        r"""Type check the input number is an integer.
        Will return True for int, SymInt and Tensors with integer elements.
        """
        if isinstance(x, (int, torch.SymInt)):
            return True
        return isinstance(x, Tensor) and not x.is_floating_point()

    @_overload  # noqa: F811
    def interpolate(input: Tensor, size: Optional[int] = None, scale_factor: Optional[List[float]] = None, mode: str = 'nearest', align_corners: Optional[bool] = None, recompute_scale_factor: Optional[bool] = None, antialias: bool = False) -> Tensor:  # noqa: F811,B950
        pass

    @_overload  # noqa: F811
    def interpolate(input: Tensor, size: Optional[List[int]] = None, scale_factor: Optional[List[float]] = None, mode: str = 'nearest', align_corners: Optional[bool] = None, recompute_scale_factor: Optional[bool] = None, antialias: bool = False) -> Tensor:  # noqa: F811,B950
        pass

    @_overload  # noqa: F811
    def interpolate(input: Tensor, size: Optional[int] = None, scale_factor: Optional[float] = None, mode: str = 'nearest', align_corners: Optional[bool] = None, recompute_scale_factor: Optional[bool] = None, antialias: bool = False) -> Tensor:  # noqa: F811,B950
        pass

    @_overload  # noqa: F811
    def interpolate(  # noqa: F811
        input: Tensor,
        size: Optional[List[int]] = None,
        scale_factor: Optional[float] = None,
        mode: str = "nearest",
        align_corners: Optional[bool] = None,
        recompute_scale_factor: Optional[bool] = None,
        antialias: bool = False,
    ) -> Tensor:  # noqa: F811
        pass

    def interpolate(input: Tensor, size: Optional[int] = None, scale_factor: Optional[List[float]] = None, mode: str = 'nearest', align_corners: Optional[bool] = None, recompute_scale_factor: Optional[bool] = None, antialias: bool = False) -> Tensor:  # noqa: F811,B950
        r"""Down/up samples the input to either the given :attr:`size` or the given
        :attr:`scale_factor`

        The algorithm used for interpolation is determined by :attr:`mode`.

        Currently temporal, spatial and volumetric sampling are supported, i.e.
        expected inputs are 3-D, 4-D or 5-D in shape.

        The input dimensions are interpreted in the form:
        `mini-batch x channels x [optional depth] x [optional height] x width`.

        The modes available for resizing are: `nearest`, `linear` (3D-only),
        `bilinear`, `bicubic` (4D-only), `trilinear` (5D-only), `area`, `nearest-exact`

        Args:
            input (Tensor): the input tensor
            size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
                output spatial size.
            scale_factor (float or Tuple[float]): multiplier for spatial size. If `scale_factor` is a tuple,
                its length has to match the number of spatial dimensions; `input.dim() - 2`.
            mode (str): algorithm used for upsampling:
                ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |
                ``'trilinear'`` | ``'area'`` | ``'nearest-exact'``. Default: ``'nearest'``
            align_corners (bool, optional): Geometrically, we consider the pixels of the
                input and output as squares rather than points.
                If set to ``True``, the input and output tensors are aligned by the
                center points of their corner pixels, preserving the values at the corner pixels.
                If set to ``False``, the input and output tensors are aligned by the corner
                points of their corner pixels, and the interpolation uses edge value padding
                for out-of-boundary values, making this operation *independent* of input size
                when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`
                is ``'linear'``, ``'bilinear'``, ``'bicubic'`` or ``'trilinear'``.
                Default: ``False``
            recompute_scale_factor (bool, optional): recompute the scale_factor for use in the
                interpolation calculation. If `recompute_scale_factor` is ``True``, then
                `scale_factor` must be passed in and `scale_factor` is used to compute the
                output `size`. The computed output `size` will be used to infer new scales for
                the interpolation. Note that when `scale_factor` is floating-point, it may differ
                from the recomputed `scale_factor` due to rounding and precision issues.
                If `recompute_scale_factor` is ``False``, then `size` or `scale_factor` will
                be used directly for interpolation. Default: ``None``.
            antialias (bool, optional): flag to apply anti-aliasing. Default: ``False``. Using anti-alias
                option together with ``align_corners=False``, interpolation result would match Pillow
                result for downsampling operation. Supported modes: ``'bilinear'``, ``'bicubic'``.

        .. note::
            With ``mode='bicubic'``, it's possible to cause overshoot, in other words it can produce
            negative values or values greater than 255 for images.
            Explicitly call ``result.clamp(min=0, max=255)`` if you want to reduce the overshoot
            when displaying the image.

        .. note::
            Mode ``mode='nearest-exact'`` matches Scikit-Image and PIL nearest neighbours interpolation
            algorithms and fixes known issues with ``mode='nearest'``. This mode is introduced to keep
            backward compatibility.
            Mode ``mode='nearest'`` matches buggy OpenCV's ``INTER_NEAREST`` interpolation algorithm.

        .. note::
            The gradients for the dtype ``float16`` on CUDA may be inaccurate in the upsample operation
            when using modes ``['linear', 'bilinear', 'bicubic', 'trilinear', 'area']``.
            For more details, please refer to the discussion in
            `issue#104157 <https://github.com/pytorch/pytorch/issues/104157>`_.

        Note:
            {backward_reproducibility_note}
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                interpolate,
                (input,),
                input,
                size=size,
                scale_factor=scale_factor,
                mode=mode,
                align_corners=align_corners,
                recompute_scale_factor=recompute_scale_factor,
                antialias=antialias
            )

        if mode in ("nearest", "area", "nearest-exact"):
            if align_corners is not None:
                raise ValueError(
                    "align_corners option can only be set with the "
                    "interpolating modes: linear | bilinear | bicubic | trilinear"
                )
        else:
            if align_corners is None:
                align_corners = False

        dim = input.dim() - 2  # Number of spatial dimensions.

        # Process size and scale_factor.  Validate that exactly one is set.
        # Validate its length if it is a list, or expand it if it is a scalar.
        # After this block, exactly one of output_size and scale_factors will
        # be non-None, and it will be a list (or tuple).
        if size is not None and scale_factor is not None:
            raise ValueError("only one of size or scale_factor should be defined")
        elif size is not None:
            assert scale_factor is None
            scale_factors = None
            if isinstance(size, (list, tuple)):
                if len(size) != dim:
                    raise ValueError(
                        "Input and output must have the same number of spatial dimensions, but got "
                        f"input with spatial dimensions of {list(input.shape[2:])} and output size of {size}. "
                        "Please provide input tensor in (N, C, d1, d2, ...,dK) format and "
                        "output size in (o1, o2, ...,oK) format."
                    )
                if not torch.jit.is_scripting():
                    if not all(_is_integer(x) for x in size):
                        raise TypeError(
                            "expected size to be one of int or Tuple[int] or Tuple[int, int] or "
                            f"Tuple[int, int, int], but got size with types {[type(x) for x in size]}"
                        )
                output_size = size
            else:
                output_size = [size for _ in range(dim)]
        elif scale_factor is not None:
            assert size is None
            output_size = None
            if isinstance(scale_factor, (list, tuple)):
                if len(scale_factor) != dim:
                    raise ValueError(
                        "Input and scale_factor must have the same number of spatial dimensions, but "
                        f"got input with spatial dimensions of {list(input.shape[2:])} and "
                        f"scale_factor of shape {scale_factor}. "
                        "Please provide input tensor in (N, C, d1, d2, ...,dK) format and "
                        "scale_factor in (s1, s2, ...,sK) format."
                    )
                scale_factors = scale_factor
            else:
                scale_factors = [scale_factor for _ in range(dim)]
        else:
            raise ValueError("either size or scale_factor should be defined")

        if recompute_scale_factor is not None and recompute_scale_factor and size is not None:
            raise ValueError("recompute_scale_factor is not meaningful with an explicit size.")

        # "area" mode always requires an explicit size rather than scale factor.
        # Re-use the recompute_scale_factor code path.
        if mode == "area" and output_size is None:
            recompute_scale_factor = True

        if recompute_scale_factor is not None and recompute_scale_factor:
            # We compute output_size here, then un-set scale_factors.
            # The C++ code will recompute it based on the (integer) output size.
            assert scale_factors is not None
            if not torch.jit.is_scripting() and torch._C._get_tracing_state():
                # make scale_factor a tensor in tracing so constant doesn't get baked in
                output_size = [
                    (torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float()))
                    for i in range(dim)
                ]
            elif torch.jit.is_scripting():
                output_size = [int(math.floor(float(input.size(i + 2)) * scale_factors[i]))
                               for i in range(dim)]
            else:
                output_size = [
                    _sym_int(input.size(i + 2) * scale_factors[i])
                    for i in range(dim)
                ]
            scale_factors = None

        if antialias and not (mode in ("bilinear", "bicubic") and input.ndim == 4):
            raise ValueError("Anti-alias option is only supported for bilinear and bicubic modes")

        if input.dim() == 3 and mode == "nearest":
            return torch._C._nn.upsample_nearest1d(input, output_size, scale_factors)
        if input.dim() == 4 and mode == "nearest":
            return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
        if input.dim() == 5 and mode == "nearest":
            return torch._C._nn.upsample_nearest3d(input, output_size, scale_factors)

        if input.dim() == 3 and mode == "nearest-exact":
            return torch._C._nn._upsample_nearest_exact1d(input, output_size, scale_factors)
        if input.dim() == 4 and mode == "nearest-exact":
            return torch._C._nn._upsample_nearest_exact2d(input, output_size, scale_factors)
        if input.dim() == 5 and mode == "nearest-exact":
            return torch._C._nn._upsample_nearest_exact3d(input, output_size, scale_factors)

        if input.dim() == 3 and mode == "area":
            assert output_size is not None
            return adaptive_avg_pool1d(input, output_size)
        if input.dim() == 4 and mode == "area":
            assert output_size is not None
            return adaptive_avg_pool2d(input, output_size)
        if input.dim() == 5 and mode == "area":
            assert output_size is not None
            return adaptive_avg_pool3d(input, output_size)

        if input.dim() == 3 and mode == "linear":
            assert align_corners is not None
            return torch._C._nn.upsample_linear1d(input, output_size, align_corners, scale_factors)
        if input.dim() == 4 and mode == "bilinear":
            assert align_corners is not None
            if antialias:
                return torch._C._nn._upsample_bilinear2d_aa(input, output_size, align_corners, scale_factors)
            # Two levels are necessary to prevent TorchScript from touching
            # are_deterministic_algorithms_enabled.
            if not torch.jit.is_scripting():
                if torch.are_deterministic_algorithms_enabled() and input.is_cuda:
                    # Use slow decomp whose backward will be in terms of index_put
                    # importlib is required because the import cannot be top level
                    # (cycle) and cannot be nested (TS doesn't support)
                    return importlib.import_module('torch._decomp.decompositions').upsample_bilinear2d_vec(
                        input, output_size, align_corners, scale_factors)
            return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, scale_factors)
        if input.dim() == 5 and mode == "trilinear":
            assert align_corners is not None
            return torch._C._nn.upsample_trilinear3d(input, output_size, align_corners, scale_factors)
        if input.dim() == 4 and mode == "bicubic":
            assert align_corners is not None
            if antialias:
                return torch._C._nn._upsample_bicubic2d_aa(input, output_size, align_corners, scale_factors)
            return torch._C._nn.upsample_bicubic2d(input, output_size, align_corners, scale_factors)

        if input.dim() == 3 and mode == "bilinear":
            raise NotImplementedError("Got 3D input, but bilinear mode needs 4D input")
        if input.dim() == 3 and mode == "trilinear":
            raise NotImplementedError("Got 3D input, but trilinear mode needs 5D input")
        if input.dim() == 4 and mode == "linear":
            raise NotImplementedError("Got 4D input, but linear mode needs 3D input")
        if input.dim() == 4 and mode == "trilinear":
            raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input")
        if input.dim() == 5 and mode == "linear":
            raise NotImplementedError("Got 5D input, but linear mode needs 3D input")
        if input.dim() == 5 and mode == "bilinear":
            raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input")

        raise NotImplementedError(
            "Input Error: Only 3D, 4D and 5D input Tensors supported"
            f" (got {input.dim()}D) for the modes: nearest | linear | bilinear | bicubic | trilinear | area | nearest-exact"
            f" (got {mode})"
        )

    if interpolate.__doc__:
        interpolate.__doc__ = interpolate.__doc__.format(**reproducibility_notes)

    @_overload  # noqa: F811
    def upsample_nearest(input: Tensor, size: Optional[int] = None, scale_factor: Optional[float] = None) -> Tensor:  # noqa: F811
        pass

    @_overload  # noqa: F811
    def upsample_nearest(input: Tensor, size: Optional[List[int]] = None, scale_factor: Optional[float] = None) -> Tensor:  # noqa: F811
        pass

    def upsample_nearest(input, size=None, scale_factor=None):  # noqa: F811
        r"""Upsamples the input, using nearest neighbours' pixel values.

        .. warning::
            This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
            This is equivalent with ``nn.functional.interpolate(..., mode='nearest')``.

        Currently spatial and volumetric upsampling are supported (i.e. expected
        inputs are 4 or 5 dimensional).

        Args:
            input (Tensor): input
            size (int or Tuple[int, int] or Tuple[int, int, int]): output spatia
                size.
            scale_factor (int): multiplier for spatial size. Has to be an integer.

        Note:
            {backward_reproducibility_note}
        """
        # DeprecationWarning is ignored by default
        warnings.warn("nn.functional.upsample_nearest is deprecated. Use nn.functional.interpolate instead.")
        return interpolate(input, size, scale_factor, mode="nearest")

    if upsample_nearest.__doc__:
        upsample_nearest.__doc__ = upsample_nearest.__doc__.format(**reproducibility_notes)

    @_overload  # noqa: F811
    def upsample_bilinear(
        input: Tensor, size: Optional[int] = None, scale_factor: Optional[float] = None
    ) -> Tensor:  # noqa: F811
        pass

    @_overload  # noqa: F811
    def upsample_bilinear(  # noqa: F811
        input: Tensor, size: Optional[List[int]] = None, scale_factor: Optional[float] = None
    ) -> Tensor:  # noqa: F811
        pass

    @_overload  # noqa: F811
    def upsample_bilinear(  # noqa: F811
        input: Tensor, size: Optional[int] = None, scale_factor: Optional[List[float]] = None
    ) -> Tensor:  # noqa: F811
        pass

    @_overload  # noqa: F811
    def upsample_bilinear(  # noqa: F811
        input: Tensor, size: Optional[List[int]] = None, scale_factor: Optional[List[float]] = None
    ) -> Tensor:  # noqa: F811
        pass

    def upsample_bilinear(input, size=None, scale_factor=None):  # noqa: F811
        r"""Upsamples the input, using bilinear upsampling.

        .. warning::
            This function is deprecated in favor of :func:`torch.nn.functional.interpolate`.
            This is equivalent with
            ``nn.functional.interpolate(..., mode='bilinear', align_corners=True)``.

        Expected inputs are spatial (4 dimensional). Use `upsample_trilinear` fo
        volumetric (5 dimensional) inputs.

        Args:
            input (Tensor): input
            size (int or Tuple[int, int]): output spatial size.
            scale_factor (int or Tuple[int, int]): multiplier for spatial size

        Note:
            {backward_reproducibility_note}
        """
        # DeprecationWarning is ignored by default
        warnings.warn("nn.functional.upsample_bilinear is deprecated. Use nn.functional.interpolate instead.")
        return interpolate(input, size, scale_factor, mode="bilinear", align_corners=True)

    if upsample_bilinear.__doc__:
        upsample_bilinear.__doc__ = upsample_bilinear.__doc__.format(**reproducibility_notes)

    GRID_SAMPLE_INTERPOLATION_MODES = {
        "bilinear": 0,
        "nearest": 1,
        "bicubic": 2,
    }

    GRID_SAMPLE_PADDING_MODES = {
        "zeros": 0,
        "border": 1,
        "reflection": 2,
    }

    def grid_sample(
        input: Tensor,
        grid: Tensor,
        mode: str = "bilinear",
        padding_mode: str = "zeros",
        align_corners: Optional[bool] = None,
    ) -> Tensor:
        r"""Given an :attr:`input` and a flow-field :attr:`grid`, computes the
        ``output`` using :attr:`input` values and pixel locations from :attr:`grid`.

        Currently, only spatial (4-D) and volumetric (5-D) :attr:`input` are
        supported.

        In the spatial (4-D) case, for :attr:`input` with shape
        :math:`(N, C, H_\text{in}, W_\text{in})` and :attr:`grid` with shape
        :math:`(N, H_\text{out}, W_\text{out}, 2)`, the output will have shape
        :math:`(N, C, H_\text{out}, W_\text{out})`.

        For each output location ``output[n, :, h, w]``, the size-2 vector
        ``grid[n, h, w]`` specifies :attr:`input` pixel locations ``x`` and ``y``,
        which are used to interpolate the output value ``output[n, :, h, w]``.
        In the case of 5D inputs, ``grid[n, d, h, w]`` specifies the
        ``x``, ``y``, ``z`` pixel locations for interpolating
        ``output[n, :, d, h, w]``. :attr:`mode` argument specifies ``nearest`` or
        ``bilinear`` interpolation method to sample the input pixels.

        :attr:`grid` specifies the sampling pixel locations normalized by the
        :attr:`input` spatial dimensions. Therefore, it should have most values in
        the range of ``[-1, 1]``. For example, values ``x = -1, y = -1`` is the
        left-top pixel of :attr:`input`, and values  ``x = 1, y = 1`` is the
        right-bottom pixel of :attr:`input`.

        If :attr:`grid` has values outside the range of ``[-1, 1]``, the corresponding
        outputs are handled as defined by :attr:`padding_mode`. Options are

            * ``padding_mode="zeros"``: use ``0`` for out-of-bound grid locations,
            * ``padding_mode="border"``: use border values for out-of-bound grid locations,
            * ``padding_mode="reflection"``: use values at locations reflected by
              the border for out-of-bound grid locations. For location far away
              from the border, it will keep being reflected until becoming in bound,
              e.g., (normalized) pixel location ``x = -3.5`` reflects by border ``-1``
              and becomes ``x' = 1.5``, then reflects by border ``1`` and becomes
              ``x'' = -0.5``.

        Note:
            This function is often used in conjunction with :func:`affine_grid`
            to build `Spatial Transformer Networks`_ .

        Note:
            When using the CUDA backend, this operation may induce nondeterministic
            behaviour in its backward pass that is not easily switched off.
            Please see the notes on :doc:`/notes/randomness` for background.

        Note:
            NaN values in :attr:`grid` would be interpreted as ``-1``.

        Args:
            input (Tensor): input of shape :math:`(N, C, H_\text{in}, W_\text{in})` (4-D case)
                            or :math:`(N, C, D_\text{in}, H_\text{in}, W_\text{in})` (5-D case)
            grid (Tensor): flow-field of shape :math:`(N, H_\text{out}, W_\text{out}, 2)` (4-D case)
                           or :math:`(N, D_\text{out}, H_\text{out}, W_\text{out}, 3)` (5-D case)
            mode (str): interpolation mode to calculate output values
                ``'bilinear'`` | ``'nearest'`` | ``'bicubic'``. Default: ``'bilinear'``
                Note: ``mode='bicubic'`` supports only 4-D input.
                When ``mode='bilinear'`` and the input is 5-D, the interpolation mode
                used internally will actually be trilinear. However, when the input is 4-D,
                the interpolation mode will legitimately be bilinear.
            padding_mode (str): padding mode for outside grid values
                ``'zeros'`` | ``'border'`` | ``'reflection'``. Default: ``'zeros'``
            align_corners (bool, optional): Geometrically, we consider the pixels of the
                input  as squares rather than points.
                If set to ``True``, the extrema (``-1`` and ``1``) are considered as referring
                to the center points of the input's corner pixels. If set to ``False``, they
                are instead considered as referring to the corner points of the input's corner
                pixels, making the sampling more resolution agnostic.
                This option parallels the ``align_corners`` option in
                :func:`interpolate`, and so whichever option is used here
                should also be used there to resize the input image before grid sampling.
                Default: ``False``

        Returns:
            output (Tensor): output Tensor

        .. _`Spatial Transformer Networks`:
            https://arxiv.org/abs/1506.02025

        .. warning::
            When ``align_corners = True``, the grid positions depend on the pixel
            size relative to the input image size, and so the locations sampled by
            :func:`grid_sample` will differ for the same input given at different
            resolutions (that is, after being upsampled or downsampled).
            The default behavior up to version 1.2.0 was ``align_corners = True``.
            Since then, the default behavior has been changed to ``align_corners = False``,
            in order to bring it in line with the default for :func:`interpolate`.

        .. note::
            ``mode='bicubic'`` is implemented using the `cubic convolution algorithm`_ with :math:`\alpha=-0.75`.
            The constant :math:`\alpha` might be different from packages to packages.
            For example, `PIL`_ and `OpenCV`_ use -0.5 and -0.75 respectively.
            This algorithm may "overshoot" the range of values it's interpolating.
            For example, it may produce negative values or values greater than 255 when interpolating input in [0, 255].
            Clamp the results with :func:`torch.clamp` to ensure they are within the valid range.
        .. _`cubic convolution algorithm`: https://en.wikipedia.org/wiki/Bicubic_interpolation
        .. _`PIL`: https://github.com/python-pillow/Pillow/blob/4634eafe3c695a014267eefdce830b4a825beed7/src/libImaging/Resample.c#L51
        .. _`OpenCV`: https://github.com/opencv/opencv/blob/f345ed564a06178670750bad59526cfa4033be55/modules/imgproc/src/resize.cpp#L908
        """
        if has_torch_function_variadic(input, grid):
            return handle_torch_function(
                grid_sample, (input, grid), input, grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners
            )
        if mode != "bilinear" and mode != "nearest" and mode != "bicubic":
            raise ValueError(
                f"nn.functional.grid_sample(): expected mode to be 'bilinear', 'nearest' or 'bicubic', but got: '{mode}'"
            )
        if padding_mode != "zeros" and padding_mode != "border" and padding_mode != "reflection":
            raise ValueError(
                "nn.functional.grid_sample(): expected padding_mode "
                "to be 'zeros', 'border', or 'reflection', "
                f"but got: '{padding_mode}'"
            )

        if mode == "bilinear":
            mode_enum = 0
        elif mode == "nearest":
            mode_enum = 1
        else:  # mode == 'bicubic'
            mode_enum = 2

        if padding_mode == "zeros":
            padding_mode_enum = 0
        elif padding_mode == "border":
            padding_mode_enum = 1
        else:  # padding_mode == 'reflection'
            padding_mode_enum = 2

        if align_corners is None:
            warnings.warn(
                "Default grid_sample and affine_grid behavior has changed "
                "to align_corners=False since 1.3.0. Please specify "
                "align_corners=True if the old behavior is desired. "
                "See the documentation of grid_sample for details."
            )
            align_corners = False

        return torch.grid_sampler(input, grid, mode_enum, padding_mode_enum, align_corners)

    def affine_grid(theta: Tensor, size: List[int], align_corners: Optional[bool] = None) -> Tensor:
        r"""Generates a 2D or 3D flow field (sampling grid), given a batch of
        affine matrices :attr:`theta`.

        .. note::
            This function is often used in conjunction with :func:`grid_sample`
            to build `Spatial Transformer Networks`_ .

        Args:
            theta (Tensor): input batch of affine matrices with shape
                (:math:`N \times 2 \times 3`) for 2D or
                (:math:`N \times 3 \times 4`) for 3D
            size (torch.Size): the target output image size.
                (:math:`N \times C \times H \times W` for 2D or
                :math:`N \times C \times D \times H \times W` for 3D)
                Example: torch.Size((32, 3, 24, 24))
            align_corners (bool, optional): if ``True``, consider ``-1`` and ``1``
                to refer to the centers of the corner pixels rather than the image corners.
                Refer to :func:`grid_sample` for a more complete description.
                A grid generated by :func:`affine_grid` should be passed to :func:`grid_sample`
                with the same setting for this option.
                Default: ``False``

        Returns:
            output (Tensor): output Tensor of size (:math:`N \times H \times W \times 2`)

        .. _`Spatial Transformer Networks`:
            https://arxiv.org/abs/1506.02025

        .. warning::
            When ``align_corners = True``, the grid positions depend on the pixel
            size relative to the input image size, and so the locations sampled by
            :func:`grid_sample` will differ for the same input given at different
            resolutions (that is, after being upsampled or downsampled).
            The default behavior up to version 1.2.0 was ``align_corners = True``.
            Since then, the default behavior has been changed to ``align_corners = False``,
            in order to bring it in line with the default for :func:`interpolate`.
        .. warning::
            When ``align_corners = True``, 2D affine transforms on 1D data and
            3D affine transforms on 2D data (that is, when one of the spatial
            dimensions has unit size) are ill-defined, and not an intended use case.
            This is not a problem when ``align_corners = False``.
            Up to version 1.2.0, all grid points along a unit dimension were
            considered arbitrarily to be at ``-1``.
            From version 1.3.0, under ``align_corners = True`` all grid points
            along a unit dimension are considered to be at ``0``
            (the center of the input image).
        """
        if has_torch_function_unary(theta):
            return handle_torch_function(affine_grid, (theta,), theta, size, align_corners=align_corners)
        if align_corners is None:
            warnings.warn(
                "Default grid_sample and affine_grid behavior has changed "
                "to align_corners=False since 1.3.0. Please specify "
                "align_corners=True if the old behavior is desired. "
                "See the documentation of grid_sample for details."
            )
            align_corners = False

        # enforce floating point dtype on theta
        if not theta.is_floating_point():
            raise ValueError(f"Expected theta to have floating point type, but got {theta.dtype}")
        # check that shapes and sizes match
        if len(size) == 4:
            if theta.dim() != 3 or theta.shape[-2] != 2 or theta.shape[-1] != 3:
                raise ValueError(
                    f"Expected a batch of 2D affine matrices of shape Nx2x3 for size {size}. Got {theta.shape}."
                )
            spatial_size = size[-2:]  # spatial dimension sizes
        elif len(size) == 5:
            if theta.dim() != 3 or theta.shape[-2] != 3 or theta.shape[-1] != 4:
                raise ValueError(
                    f"Expected a batch of 3D affine matrices of shape Nx3x4 for size {size}. Got {theta.shape}."
                )
            spatial_size = size[-3:]  # spatial dimension sizes
        else:
            raise NotImplementedError(
                "affine_grid only supports 4D and 5D sizes, "
                "for 2D and 3D affine transforms, respectively. "
                f"Got size {size}."
            )
        # check for empty span
        if align_corners and min(spatial_size) == 1:
            warnings.warn(
                "Since version 1.3.0, affine_grid behavior has changed "
                "for unit-size grids when align_corners=True. "
                "This is not an intended use case of affine_grid. "
                "See the documentation of affine_grid for details."
            )
        elif min(size) <= 0:
            raise ValueError(f"Expected non-zero, positive output size. Got {size}")

        return torch.affine_grid_generator(theta, size, align_corners)

    pad = _add_docstr(
        torch._C._nn.pad,
        r"""
    pad(input, pad, mode="constant", value=None) -> Tensor

    Pads tensor.

    Padding size:
        The padding size by which to pad some dimensions of :attr:`input`
        are described starting from the last dimension and moving forward.
        :math:`\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor` dimensions
        of ``input`` will be padded.
        For example, to pad only the last dimension of the input tensor, then
        :attr:`pad` has the form
        :math:`(\text{padding\_left}, \text{padding\_right})`;
        to pad the last 2 dimensions of the input tensor, then use
        :math:`(\text{padding\_left}, \text{padding\_right},`
        :math:`\text{padding\_top}, \text{padding\_bottom})`;
        to pad the last 3 dimensions, use
        :math:`(\text{padding\_left}, \text{padding\_right},`
        :math:`\text{padding\_top}, \text{padding\_bottom}`
        :math:`\text{padding\_front}, \text{padding\_back})`.

    Padding mode:
        See :class:`torch.nn.CircularPad2d`, :class:`torch.nn.ConstantPad2d`,
        :class:`torch.nn.ReflectionPad2d`, and :class:`torch.nn.ReplicationPad2d`
        for concrete examples on how each of the padding modes works. Constant
        padding is implemented for arbitrary dimensions. Circular, replicate and
        reflection padding are implemented for padding the last 3 dimensions of a
        4D or 5D input tensor, the last 2 dimensions of a 3D or 4D input tensor,
        or the last dimension of a 2D or 3D input tensor.

    Note:
        When using the CUDA backend, this operation may induce nondeterministic
        behaviour in its backward pass that is not easily switched off.
        Please see the notes on :doc:`/notes/randomness` for background.

    Args:
        input (Tensor): N-dimensional tensor
        pad (tuple): m-elements tuple, where
            :math:`\frac{m}{2} \leq` input dimensions and :math:`m` is even.
        mode: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``.
            Default: ``'constant'``
        value: fill value for ``'constant'`` padding. Default: ``0``

    Examples::

        >>> t4d = torch.empty(3, 3, 4, 2)
        >>> p1d = (1, 1) # pad last dim by 1 on each side
        >>> out = F.pad(t4d, p1d, "constant", 0)  # effectively zero padding
        >>> print(out.size())
        torch.Size([3, 3, 4, 4])
        >>> p2d = (1, 1, 2, 2) # pad last dim by (1, 1) and 2nd to last by (2, 2)
        >>> out = F.pad(t4d, p2d, "constant", 0)
        >>> print(out.size())
        torch.Size([3, 3, 8, 4])
        >>> t4d = torch.empty(3, 3, 4, 2)
        >>> p3d = (0, 1, 2, 1, 3, 3) # pad by (0, 1), (2, 1), and (3, 3)
        >>> out = F.pad(t4d, p3d, "constant", 0)
        >>> print(out.size())
        torch.Size([3, 9, 7, 3])

    """)
    # TODO: Fix via https://github.com/pytorch/pytorch/issues/75798
    pad.__module__ = "torch.nn.functional"

    # distance

    pairwise_distance = _add_docstr(
        torch.pairwise_distance,
        r"""
    pairwise_distance(x1, x2, p=2.0, eps=1e-6, keepdim=False) -> Tensor

    See :class:`torch.nn.PairwiseDistance` for details
    """)

    pdist = _add_docstr(
        torch.pdist,
        r"""
    pdist(input, p=2) -> Tensor

    Computes the p-norm distance between every pair of row vectors in the input.
    This is identical to the upper triangular portion, excluding the diagonal, of
    `torch.norm(input[:, None] - input, dim=2, p=p)`. This function will be faster
    if the rows are contiguous.

    If input has shape :math:`N \times M` then the output will have shape
    :math:`\frac{1}{2} N (N - 1)`.

    This function is equivalent to ``scipy.spatial.distance.pdist(input,
    'minkowski', p=p)`` if :math:`p \in (0, \infty)`. When :math:`p = 0` it is
    equivalent to ``scipy.spatial.distance.pdist(input, 'hamming') * M``.
    When :math:`p = \infty`, the closest scipy function is
    ``scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())``.

    Args:
        input: input tensor of shape :math:`N \times M`.
        p: p value for the p-norm distance to calculate between each vector pair
            :math:`\in [0, \infty]`.
    """,
    )

    cosine_similarity = _add_docstr(
        torch.cosine_similarity,
        r"""
    cosine_similarity(x1, x2, dim=1, eps=1e-8) -> Tensor

    Returns cosine similarity between ``x1`` and ``x2``, computed along dim. ``x1`` and ``x2`` must be broadcastable
    to a common shape. ``dim`` refers to the dimension in this common shape. Dimension ``dim`` of the output is
    squeezed (see :func:`torch.squeeze`), resulting in the
    output tensor having 1 fewer dimension.

    .. math ::
        \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2, \epsilon) \cdot \max(\Vert x_2 \Vert _2, \epsilon)}

    Supports :ref:`type promotion <type-promotion-doc>`.

    Args:
        x1 (Tensor): First input.
        x2 (Tensor): Second input.
        dim (int, optional): Dimension along which cosine similarity is computed. Default: 1
        eps (float, optional): Small value to avoid division by zero.
            Default: 1e-8

    Example::

        >>> input1 = torch.randn(100, 128)
        >>> input2 = torch.randn(100, 128)
        >>> output = F.cosine_similarity(input1, input2)
        >>> print(output)
    """,
    )

    one_hot = _add_docstr(
        torch._C._nn.one_hot,
        r"""
    one_hot(tensor, num_classes=-1) -> LongTensor

    Takes LongTensor with index values of shape ``(*)`` and returns a tensor
    of shape ``(*, num_classes)`` that have zeros everywhere except where the
    index of last dimension matches the corresponding value of the input tensor,
    in which case it will be 1.

    See also `One-hot on Wikipedia`_ .

    .. _One-hot on Wikipedia:
        https://en.wikipedia.org/wiki/One-hot

    Arguments:
        tensor (LongTensor): class values of any shape.
        num_classes (int):  Total number of classes. If set to -1, the number
            of classes will be inferred as one greater than the largest class
            value in the input tensor.

    Returns:
        LongTensor that has one more dimension with 1 values at the
        index of last dimension indicated by the input, and 0 everywhere
        else.

    Examples:
        >>> F.one_hot(torch.arange(0, 5) % 3)
        tensor([[1, 0, 0],
                [0, 1, 0],
                [0, 0, 1],
                [1, 0, 0],
                [0, 1, 0]])
        >>> F.one_hot(torch.arange(0, 5) % 3, num_classes=5)
        tensor([[1, 0, 0, 0, 0],
                [0, 1, 0, 0, 0],
                [0, 0, 1, 0, 0],
                [1, 0, 0, 0, 0],
                [0, 1, 0, 0, 0]])
        >>> F.one_hot(torch.arange(0, 6).view(3,2) % 3)
        tensor([[[1, 0, 0],
                 [0, 1, 0]],
                [[0, 0, 1],
                 [1, 0, 0]],
                [[0, 1, 0],
                 [0, 0, 1]]])
    """,
    )

    def triplet_margin_loss(
        anchor: Tensor,
        positive: Tensor,
        negative: Tensor,
        margin: float = 1.0,
        p: float = 2,
        eps: float = 1e-6,
        swap: bool = False,
        size_average: Optional[bool] = None,
        reduce: Optional[bool] = None,
        reduction: str = "mean",
    ) -> Tensor:
        r"""
        See :class:`~torch.nn.TripletMarginLoss` for details
        """
        if has_torch_function_variadic(anchor, positive, negative):
            return handle_torch_function(
                triplet_margin_loss,
                (anchor, positive, negative),
                anchor,
                positive,
                negative,
                margin=margin,
                p=p,
                eps=eps,
                swap=swap,
                size_average=size_average,
                reduce=reduce,
                reduction=reduction,
            )
        if size_average is not None or reduce is not None:
            reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)
        else:
            reduction_enum = _Reduction.get_enum(reduction)
        return torch.triplet_margin_loss(anchor, positive, negative, margin, p, eps, swap, reduction_enum)

    def triplet_margin_with_distance_loss(
        anchor: Tensor,
        positive: Tensor,
        negative: Tensor,
        *,
        distance_function: Optional[Callable[[Tensor, Tensor], Tensor]] = None,
        margin: float = 1.0,
        swap: bool = False,
        reduction: str = "mean"
    ) -> Tensor:
        r"""
        See :class:`~torch.nn.TripletMarginWithDistanceLoss` for details.
        """
        if torch.jit.is_scripting():
            raise NotImplementedError(
                "F.triplet_margin_with_distance_loss does not support JIT scripting: "
                "functions requiring Callables cannot be scripted."
            )

        if has_torch_function_variadic(anchor, positive, negative):
            return handle_torch_function(
                triplet_margin_with_distance_loss,
                (anchor, positive, negative),
                anchor,
                positive,
                negative,
                distance_function=distance_function,
                margin=margin,
                swap=swap,
                reduction=reduction,
            )

        # Check validity of reduction mode
        if reduction not in ("mean", "sum", "none"):
            raise ValueError(f"{reduction} is not a valid value for reduction")

        # Check dimensions
        a_dim = anchor.ndim
        p_dim = positive.ndim
        n_dim = negative.ndim
        if not (a_dim == p_dim and p_dim == n_dim):
            raise RuntimeError(
                f"The anchor, positive, and negative tensors are expected to have "
                f"the same number of dimensions, but got: anchor {a_dim}D, "
                f"positive {p_dim}D, and negative {n_dim}D inputs")

        # Calculate loss
        if distance_function is None:
            distance_function = torch.pairwise_distance

        dist_pos = distance_function(anchor, positive)
        dist_neg = distance_function(anchor, negative)
        # The distance swap is described in the paper "Learning shallow
        # convolutional feature descriptors with triplet losses" by V. Balntas, E.
        # Riba et al.  If True, and if the positive example is closer to the
        # negative example than the anchor is, swaps the positive example and the
        # anchor in the loss computation.
        if swap:
            dist_swap = distance_function(positive, negative)
            dist_neg = torch.minimum(dist_neg, dist_swap)
        loss = torch.clamp_min(margin + dist_pos - dist_neg, 0)

        # Apply reduction
        if reduction == "sum":
            return torch.sum(loss)
        elif reduction == "mean":
            return torch.mean(loss)
        else:  # reduction == "none"
            return loss

    def normalize(input: Tensor, p: float = 2.0, dim: int = 1, eps: float = 1e-12, out: Optional[Tensor] = None) -> Tensor:
        r"""Performs :math:`L_p` normalization of inputs over specified dimension.

        For a tensor :attr:`input` of sizes :math:`(n_0, ..., n_{dim}, ..., n_k)`, each
        :math:`n_{dim}` -element vector :math:`v` along dimension :attr:`dim` is transformed as

        .. math::
            v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}.

        With the default arguments it uses the Euclidean norm over vectors along dimension :math:`1` for normalization.

        Args:
            input: input tensor of any shape
            p (float): the exponent value in the norm formulation. Default: 2
            dim (int or tuple of ints): the dimension to reduce. Default: 1
            eps (float): small value to avoid division by zero. Default: 1e-12
            out (Tensor, optional): the output tensor. If :attr:`out` is used, this
                                    operation won't be differentiable.
        """
        if has_torch_function_variadic(input, out):
            return handle_torch_function(normalize, (input, out), input, p=p, dim=dim, eps=eps, out=out)
        if out is None:
            denom = input.norm(p, dim, keepdim=True).clamp_min(eps).expand_as(input)
            return input / denom
        else:
            denom = input.norm(p, dim, keepdim=True).clamp_min_(eps).expand_as(input)
            return torch.div(input, denom, out=out)

    def assert_int_or_pair(arg: List[int], arg_name: str, message: str) -> None:
        assert isinstance(arg, int) or len(arg) == 2, message.format(arg_name)

    def unfold(
        input: Tensor, kernel_size: BroadcastingList2[int],
        dilation: BroadcastingList2[int] = 1,
        padding: BroadcastingList2[int] = 0,
        stride: BroadcastingList2[int] = 1
    ) -> Tensor:
        r"""Extracts sliding local blocks from a batched input tensor.

        .. warning::
            Currently, only 4-D input tensors (batched image-like tensors) are
            supported.

        .. warning::

            More than one element of the unfolded tensor may refer to a single
            memory location. As a result, in-place operations (especially ones that
            are vectorized) may result in incorrect behavior. If you need to write
            to the tensor, please clone it first.

        See :class:`torch.nn.Unfold` for details
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                unfold, (input,), input, kernel_size, dilation=dilation, padding=padding, stride=stride
            )
        return torch._C._nn.im2col(input, _pair(kernel_size), _pair(dilation), _pair(padding), _pair(stride))

    def fold(
        input: Tensor, output_size: BroadcastingList2[int],
        kernel_size: BroadcastingList2[int],
        dilation: BroadcastingList2[int] = 1,
        padding: BroadcastingList2[int] = 0,
        stride: BroadcastingList2[int] = 1
    ) -> Tensor:
        r"""Combines an array of sliding local blocks into a large containing
        tensor.

        .. warning::
            Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported.

        See :class:`torch.nn.Fold` for details
        """
        if has_torch_function_unary(input):
            return handle_torch_function(
                fold, (input,), input, output_size, kernel_size, dilation=dilation, padding=padding, stride=stride
            )
        return torch._C._nn.col2im(
            input, _pair(output_size), _pair(kernel_size), _pair(dilation), _pair(padding), _pair(stride)
        )

    #
    # multihead attention
    #

    def _in_projection_packed(
        q: Tensor,
        k: Tensor,
        v: Tensor,
        w: Tensor,
        b: Optional[Tensor] = None,
    ) -> List[Tensor]:
        r"""
        Performs the in-projection step of the attention operation, using packed weights.
        Output is a triple containing projection tensors for query, key and value.

        Args:
            q, k, v: query, key and value tensors to be projected. For self-attention,
                these are typically the same tensor; for encoder-decoder attention,
                k and v are typically the same tensor. (We take advantage of these
                identities for performance if they are present.) Regardless, q, k and v
                must share a common embedding dimension; otherwise their shapes may vary.
            w: projection weights for q, k and v, packed into a single tensor. Weights
                are packed along dimension 0, in q, k, v order.
            b: optional projection biases for q, k and v, packed into a single tensor
                in q, k, v order.

        Shape:
            Inputs:
            - q: :math:`(..., E)` where E is the embedding dimension
            - k: :math:`(..., E)` where E is the embedding dimension
            - v: :math:`(..., E)` where E is the embedding dimension
            - w: :math:`(E * 3, E)` where E is the embedding dimension
            - b: :math:`E * 3` where E is the embedding dimension

            Output:
            - in output list :math:`[q', k', v']`, each output tensor will have the
                same shape as the corresponding input tensor.
        """
        E = q.size(-1)
        if k is v:
            if q is k:
                # self-attention
                proj = linear(q, w, b)
                # reshape to 3, E and not E, 3 is deliberate for better memory coalescing and keeping same order as chunk()
                proj = proj.unflatten(-1, (3, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
                return proj[0], proj[1], proj[2]
            else:
                # encoder-decoder attention
                w_q, w_kv = w.split([E, E * 2])
                if b is None:
                    b_q = b_kv = None
                else:
                    b_q, b_kv = b.split([E, E * 2])
                q_proj = linear(q, w_q, b_q)
                kv_proj = linear(k, w_kv, b_kv)
                # reshape to 2, E and not E, 2 is deliberate for better memory coalescing and keeping same order as chunk()
                kv_proj = kv_proj.unflatten(-1, (2, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
                return (q_proj, kv_proj[0], kv_proj[1])
        else:
            w_q, w_k, w_v = w.chunk(3)
            if b is None:
                b_q = b_k = b_v = None
            else:
                b_q, b_k, b_v = b.chunk(3)
            return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)

    def _in_projection(
        q: Tensor,
        k: Tensor,
        v: Tensor,
        w_q: Tensor,
        w_k: Tensor,
        w_v: Tensor,
        b_q: Optional[Tensor] = None,
        b_k: Optional[Tensor] = None,
        b_v: Optional[Tensor] = None,
    ) -> Tuple[Tensor, Tensor, Tensor]:
        r"""
        Performs the in-projection step of the attention operation. This is simply
        a triple of linear projections, with shape constraints on the weights which
        ensure embedding dimension uniformity in the projected outputs.
        Output is a triple containing projection tensors for query, key and value.

        Args:
            q, k, v: query, key and value tensors to be projected.
            w_q, w_k, w_v: weights for q, k and v, respectively.
            b_q, b_k, b_v: optional biases for q, k and v, respectively.

        Shape:
            Inputs:
            - q: :math:`(Qdims..., Eq)` where Eq is the query embedding dimension and Qdims are any
                number of leading dimensions.
            - k: :math:`(Kdims..., Ek)` where Ek is the key embedding dimension and Kdims are any
                number of leading dimensions.
            - v: :math:`(Vdims..., Ev)` where Ev is the value embedding dimension and Vdims are any
                number of leading dimensions.
            - w_q: :math:`(Eq, Eq)`
            - w_k: :math:`(Eq, Ek)`
            - w_v: :math:`(Eq, Ev)`
            - b_q: :math:`(Eq)`
            - b_k: :math:`(Eq)`
            - b_v: :math:`(Eq)`

            Output: in output triple :math:`(q', k', v')`,
             - q': :math:`[Qdims..., Eq]`
             - k': :math:`[Kdims..., Eq]`
             - v': :math:`[Vdims..., Eq]`

        """
        Eq, Ek, Ev = q.size(-1), k.size(-1), v.size(-1)
        assert w_q.shape == (Eq, Eq), f"expecting query weights shape of {(Eq, Eq)}, but got {w_q.shape}"
        assert w_k.shape == (Eq, Ek), f"expecting key weights shape of {(Eq, Ek)}, but got {w_k.shape}"
        assert w_v.shape == (Eq, Ev), f"expecting value weights shape of {(Eq, Ev)}, but got {w_v.shape}"
        assert b_q is None or b_q.shape == (Eq,), f"expecting query bias shape of {(Eq,)}, but got {b_q.shape}"
        assert b_k is None or b_k.shape == (Eq,), f"expecting key bias shape of {(Eq,)}, but got {b_k.shape}"
        assert b_v is None or b_v.shape == (Eq,), f"expecting value bias shape of {(Eq,)}, but got {b_v.shape}"
        return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)

    scaled_dot_product_attention = _add_docstr(
        torch._C._nn.scaled_dot_product_attention, r"""
    scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None) -> Tensor:

    Computes scaled dot product attention on query, key and value tensors, using
    an optional attention mask if passed, and applying dropout if a probability
    greater than 0.0 is specified.

    .. code-block:: python

        # Efficient implementation equivalent to the following:
        def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None) -> torch.Tensor:
            # Efficient implementation equivalent to the following:
            L, S = query.size(-2), key.size(-2)
            scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
            attn_bias = torch.zeros(L, S, dtype=query.dtype)
            if is_causal:
                assert attn_mask is None
                temp_mask = torch.ones(L, S, dtype=torch.bool).tril(diagonal=0)
                attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
                attn_bias.to(query.dtype)

            if attn_mask is not None:
                if attn_mask.dtype == torch.bool:
                    attn_mask.masked_fill_(attn_mask.logical_not(), float("-inf"))
                else:
                    attn_bias += attn_mask
            attn_weight = query @ key.transpose(-2, -1) * scale_factor
            attn_weight += attn_bias
            attn_weight = torch.softmax(attn_weight, dim=-1)
            attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
            return attn_weight @ value

    .. warning:: This function is beta and subject to change.

    Note:

        There are currently three supported implementations of scaled dot product attention:

            - `FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness`_
            - `Memory-Efficient Attention`_
            - A PyTorch implementation defined in C++ matching the above formulation

        The function may call optimized kernels for improved performance when using the CUDA backend.
        For all other backends, the PyTorch implementation will be used.

        All implementations are enabled by default. Scaled dot product attention attempts to automatically select the
        most optimal implementation based on the inputs. In order to provide more fine-grained control over what implementation
        is used, the following functions are provided for enabling and disabling implementations.
        The context manager is the preferred mechanism:

            - :func:`torch.backends.cuda.sdp_kernel`: A context manager used to enable/disable any of the implementations.
            - :func:`torch.backends.cuda.enable_flash_sdp`: Enables or Disables FlashAttention.
            - :func:`torch.backends.cuda.enable_mem_efficient_sdp`: Enables or Disables Memory-Efficient Attention.
            - :func:`torch.backends.cuda.enable_math_sdp`: Enables or Disables the PyTorch C++ implementation.

        Each of the fused kernels has specific input limitations. If the user requires the use of a specific fused implementation,
        disable the PyTorch C++ implementation using :func:`torch.backends.cuda.sdp_kernel`.
        In the event that a fused implementation is not available, an error will be raised with the
        reasons why the fused implementation cannot run.

        Due to the nature of fusing floating point operations, the output of this function may be different
        depending on what backend kernel is chosen.
        The c++ implementation supports torch.float64 and can be used when higher precision is required.
        For more information please see :doc:`/notes/numerical_accuracy`

    Note:
        {cudnn_reproducibility_note}
    """.format(**reproducibility_notes)
        + r"""

    Args:
        query (Tensor): Query tensor; shape :math:`(N, ..., L, E)`.
        key (Tensor): Key tensor; shape :math:`(N, ..., S, E)`.
        value (Tensor): Value tensor; shape :math:`(N, ..., S, Ev)`.
        attn_mask (optional Tensor): Attention mask; shape :math:`(N, ..., L, S)`. Two types of masks are supported.
            A boolean mask where a value of True indicates that the element *should* take part in attention.
            A float mask of the same type as query, key, value that is added to the attention score.
        dropout_p (float): Dropout probability; if greater than 0.0, dropout is applied
        is_causal (bool): If true, assumes causal attention masking and errors if both attn_mask and is_causal
            are set.
        scale (optional float): Scaling factor applied prior to softmax. If None, the default value is set
            to :math:`\frac{1}{\sqrt{E}}`.

    Returns:
        output (Tensor): Attention output; shape :math:`(N, ..., L, Ev)`.

    Shape legend:
        - :math:`N: \text{Batch size} ... : \text{Any number of other batch dimensions (optional)}`
        - :math:`S: \text{Source sequence length}`
        - :math:`L: \text{Target sequence length}`
        - :math:`E: \text{Embedding dimension of the query and key}`
        - :math:`Ev: \text{Embedding dimension of the value}`

    Examples::

        >>> # Optionally use the context manager to ensure one of the fused kernels is run
        >>> query = torch.rand(32, 8, 128, 64, dtype=torch.float16, device="cuda")
        >>> key = torch.rand(32, 8, 128, 64, dtype=torch.float16, device="cuda")
        >>> value = torch.rand(32, 8, 128, 64, dtype=torch.float16, device="cuda")
        >>> with torch.backends.cuda.sdp_kernel(enable_math=False):
        >>>     F.scaled_dot_product_attention(query,key,value)

    .. _FlashAttention\: Fast and Memory-Efficient Exact Attention with IO-Awareness:
        https://arxiv.org/abs/2205.14135
    .. _Memory-Efficient Attention:
        https://github.com/facebookresearch/xformers

    """)

    def _mha_shape_check(query: Tensor, key: Tensor, value: Tensor,
                         key_padding_mask: Optional[Tensor], attn_mask: Optional[Tensor], num_heads: int):
        # Verifies the expected shape for `query, `key`, `value`, `key_padding_mask` and `attn_mask`
        # and returns if the input is batched or not.
        # Raises an error if `query` is not 2-D (unbatched) or 3-D (batched) tensor.

        # Shape check.
        if query.dim() == 3:
            # Batched Inputs
            is_batched = True
            assert key.dim() == 3 and value.dim() == 3, \
                ("For batched (3-D) `query`, expected `key` and `value` to be 3-D"
                 f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")
            if key_padding_mask is not None:
                assert key_padding_mask.dim() == 2, \
                    ("For batched (3-D) `query`, expected `key_padding_mask` to be `None` or 2-D"
                     f" but found {key_padding_mask.dim()}-D tensor instead")
            if attn_mask is not None:
                assert attn_mask.dim() in (2, 3), \
                    ("For batched (3-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
                     f" but found {attn_mask.dim()}-D tensor instead")
        elif query.dim() == 2:
            # Unbatched Inputs
            is_batched = False
            assert key.dim() == 2 and value.dim() == 2, \
                ("For unbatched (2-D) `query`, expected `key` and `value` to be 2-D"
                 f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")

            if key_padding_mask is not None:
                assert key_padding_mask.dim() == 1, \
                    ("For unbatched (2-D) `query`, expected `key_padding_mask` to be `None` or 1-D"
                     f" but found {key_padding_mask.dim()}-D tensor instead")

            if attn_mask is not None:
                assert attn_mask.dim() in (2, 3), \
                    ("For unbatched (2-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
                     f" but found {attn_mask.dim()}-D tensor instead")
                if attn_mask.dim() == 3:
                    expected_shape = (num_heads, query.shape[0], key.shape[0])
                    assert attn_mask.shape == expected_shape, \
                        (f"Expected `attn_mask` shape to be {expected_shape} but got {attn_mask.shape}")
        else:
            raise AssertionError(
                f"query should be unbatched 2D or batched 3D tensor but received {query.dim()}-D query tensor")

        return is_batched

    def _canonical_mask(
            mask: Optional[Tensor],
            mask_name: str,
            other_type: Optional[DType],
            other_name: str,
            target_type: DType,
            check_other: bool = True,
    ) -> Optional[Tensor]:

        if mask is not None:
            _mask_dtype = mask.dtype
            _mask_is_float = torch.is_floating_point(mask)
            if _mask_dtype != torch.bool and not _mask_is_float:
                raise AssertionError(
                    f"only bool and floating types of {mask_name} are supported")
            if check_other and other_type is not None:
                if _mask_dtype != other_type:
                    warnings.warn(
                        f"Support for mismatched {mask_name} and {other_name} "
                        "is deprecated. Use same type for both instead."
                    )
            if not _mask_is_float:
                mask = (
                    torch.zeros_like(mask, dtype=target_type)
                    .masked_fill_(mask, float("-inf"))
                )
        return mask

    def _none_or_dtype(input: Optional[Tensor]) -> Optional[DType]:
        if input is None:
            return None
        elif isinstance(input, torch.Tensor):
            return input.dtype
        raise RuntimeError("input to _none_or_dtype() must be None or torch.Tensor")

    def multi_head_attention_forward(
        query: Tensor,
        key: Tensor,
        value: Tensor,
        embed_dim_to_check: int,
        num_heads: int,
        in_proj_weight: Optional[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] = None,
        need_weights: bool = True,
        attn_mask: Optional[Tensor] = None,
        use_separate_proj_weight: bool = False,
        q_proj_weight: Optional[Tensor] = None,
        k_proj_weight: Optional[Tensor] = None,
        v_proj_weight: Optional[Tensor] = None,
        static_k: Optional[Tensor] = None,
        static_v: Optional[Tensor] = None,
        average_attn_weights: bool = True,
        is_causal: bool = False,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Args:
            query, key, value: map a query and a set of key-value pairs to an output.
                See "Attention Is All You Need" for more details.
            embed_dim_to_check: total dimension of the model.
            num_heads: parallel attention heads.
            in_proj_weight, in_proj_bias: input projection weight and bias.
            bias_k, bias_v: bias of the key and value sequences to be added at dim=0.
            add_zero_attn: add a new batch of zeros to the key and
                           value sequences at dim=1.
            dropout_p: probability of an element to be zeroed.
            out_proj_weight, out_proj_bias: the output projection weight and bias.
            training: apply dropout if is ``True``.
            key_padding_mask: if provided, specified padding elements in the key will
                be ignored by the attention. This is an binary mask. When the value is True,
                the corresponding value on the attention layer will be filled with -inf.
            need_weights: output attn_output_weights.
                Default: `True`
                Note: `needs_weight` defaults to `True`, but should be set to `False`
                For best performance when attention weights are not needed.
                *Setting needs_weights to `True`
                leads to a significant performance degradation.*
            attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
                the batches while a 3D mask allows to specify a different mask for the entries of each batch.
            is_causal: If specified, applies a causal mask as attention mask, and ignores
                attn_mask for computing scaled dot product attention.
                Default: ``False``.
                .. warning::
                    is_causal is provides a hint that the attn_mask is the
                    causal mask.Providing incorrect hints can result in
                    incorrect execution, including forward and backward
                    compatibility.
            use_separate_proj_weight: the function accept the proj. weights for query, key,
                and value in different forms. If false, in_proj_weight will be used, which is
                a combination of q_proj_weight, k_proj_weight, v_proj_weight.
            q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.
            static_k, static_v: static key and value used for attention operators.
            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across heads.
                Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an effect
                when ``need_weights=True.``. Default: True

        Shape:
            Inputs:
            - query: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
              the embedding dimension.
            - key: :math:`(S, E)` or :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
              the embedding dimension.
            - value: :math:`(S, E)` or :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
              the embedding dimension.
            - key_padding_mask: :math:`(S)` or :math:`(N, S)` where N is the batch size, S is the source sequence length.
              If a FloatTensor is provided, it will be directly added to the value.
              If a BoolTensor is provided, the positions with the
              value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
            - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
              3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
              S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked
              positions. If a BoolTensor is provided, positions with ``True``
              are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
              is provided, it will be added to the attention weight.
            - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
              N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
            - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
              N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.

            Outputs:
            - attn_output: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
              E is the embedding dimension.
            - attn_output_weights: Only returned when ``need_weights=True``. If ``average_attn_weights=True``, returns
              attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
              head of shape :math:`(num_heads, L, S)` when input is unbatched or :math:`(N, num_heads, L, S)`.
        """
        tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, out_proj_weight, out_proj_bias)
        if has_torch_function(tens_ops):
            return handle_torch_function(
                multi_head_attention_forward,
                tens_ops,
                query,
                key,
                value,
                embed_dim_to_check,
                num_heads,
                in_proj_weight,
                in_proj_bias,
                bias_k,
                bias_v,
                add_zero_attn,
                dropout_p,
                out_proj_weight,
                out_proj_bias,
                training=training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=attn_mask,
                is_causal=is_causal,
                use_separate_proj_weight=use_separate_proj_weight,
                q_proj_weight=q_proj_weight,
                k_proj_weight=k_proj_weight,
                v_proj_weight=v_proj_weight,
                static_k=static_k,
                static_v=static_v,
                average_attn_weights=average_attn_weights,
            )

        is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)

        # For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
        # is batched, run the computation and before returning squeeze the
        # batch dimension so that the output doesn't carry this temporary batch dimension.
        if not is_batched:
            # unsqueeze if the input is unbatched
            query = query.unsqueeze(1)
            key = key.unsqueeze(1)
            value = value.unsqueeze(1)
            if key_padding_mask is not None:
                key_padding_mask = key_padding_mask.unsqueeze(0)

        # set up shape vars
        tgt_len, bsz, embed_dim = query.shape
        src_len, _, _ = key.shape

        key_padding_mask = _canonical_mask(
            mask=key_padding_mask,
            mask_name="key_padding_mask",
            other_type=_none_or_dtype(attn_mask),
            other_name="attn_mask",
            target_type=query.dtype
        )

        if is_causal and attn_mask is None:
            raise RuntimeError(
                "Need attn_mask if specifying the is_causal hint. "
                "You may use the Transformer module method "
                "`generate_square_subsequent_mask` to create this mask."
            )

        if is_causal and key_padding_mask is None and not need_weights:
            # when we have a kpm or need weights, we need attn_mask
            # Otherwise, we use the is_causal hint go as is_causal
            # indicator to SDPA.
            attn_mask = None
        else:
            attn_mask = _canonical_mask(
                mask=attn_mask,
                mask_name="attn_mask",
                other_type=None,
                other_name="",
                target_type=query.dtype,
                check_other=False,
            )

            if key_padding_mask is not None:
                # We have the attn_mask, and use that to merge kpm into it.
                # Turn off use of is_causal hint, as the merged mask is no
                # longer causal.
                is_causal = False

        assert embed_dim == embed_dim_to_check, \
            f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
        if isinstance(embed_dim, torch.Tensor):
            # embed_dim can be a tensor when JIT tracing
            head_dim = embed_dim.div(num_heads, rounding_mode='trunc')
        else:
            head_dim = embed_dim // num_heads
        assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
        if use_separate_proj_weight:
            # allow MHA to have different embedding dimensions when separate projection weights are used
            assert key.shape[:2] == value.shape[:2], \
                f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
        else:
            assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"

        #
        # compute in-projection
        #
        if not use_separate_proj_weight:
            assert in_proj_weight is not None, "use_separate_proj_weight is False but in_proj_weight is None"
            q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
        else:
            assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
            assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
            assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
            if in_proj_bias is None:
                b_q = b_k = b_v = None
            else:
                b_q, b_k, b_v = in_proj_bias.chunk(3)
            q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v)

        # prep attention mask

        if attn_mask is not None:
            # ensure attn_mask's dim is 3
            if attn_mask.dim() == 2:
                correct_2d_size = (tgt_len, src_len)
                if attn_mask.shape != correct_2d_size:
                    raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
                attn_mask = attn_mask.unsqueeze(0)
            elif attn_mask.dim() == 3:
                correct_3d_size = (bsz * num_heads, tgt_len, src_len)
                if attn_mask.shape != correct_3d_size:
                    raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
            else:
                raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")

        # add bias along batch dimension (currently second)
        if bias_k is not None and bias_v is not None:
            assert static_k is None, "bias cannot be added to static key."
            assert static_v is None, "bias cannot be added to static value."
            k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
            v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
            if attn_mask is not None:
                attn_mask = pad(attn_mask, (0, 1))
            if key_padding_mask is not None:
                key_padding_mask = pad(key_padding_mask, (0, 1))
        else:
            assert bias_k is None
            assert bias_v is None

        #
        # reshape q, k, v for multihead attention and make em batch first
        #
        q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
        if static_k is None:
            k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
        else:
            # TODO finish disentangling control flow so we don't do in-projections when statics are passed
            assert static_k.size(0) == bsz * num_heads, \
                f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
            assert static_k.size(2) == head_dim, \
                f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
            k = static_k
        if static_v is None:
            v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
        else:
            # TODO finish disentangling control flow so we don't do in-projections when statics are passed
            assert static_v.size(0) == bsz * num_heads, \
                f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
            assert static_v.size(2) == head_dim, \
                f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
            v = static_v

        # add zero attention along batch dimension (now first)
        if add_zero_attn:
            zero_attn_shape = (bsz * num_heads, 1, head_dim)
            k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
            v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
            if attn_mask is not None:
                attn_mask = pad(attn_mask, (0, 1))
            if key_padding_mask is not None:
                key_padding_mask = pad(key_padding_mask, (0, 1))

        # update source sequence length after adjustments
        src_len = k.size(1)

        # merge key padding and attention masks
        if key_padding_mask is not None:
            assert key_padding_mask.shape == (bsz, src_len), \
                f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
            key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len).   \
                expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
            if attn_mask is None:
                attn_mask = key_padding_mask
            else:
                attn_mask = attn_mask + key_padding_mask

        # adjust dropout probability
        if not training:
            dropout_p = 0.0

        #
        # (deep breath) calculate attention and out projection
        #

        if need_weights:
            B, Nt, E = q.shape
            q_scaled = q / math.sqrt(E)

            assert not (is_causal and attn_mask is None), "FIXME: is_causal not implemented for need_weights"

            if attn_mask is not None:
                attn_output_weights = torch.baddbmm(attn_mask, q_scaled, k.transpose(-2, -1))
            else:
                attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1))
            attn_output_weights = softmax(attn_output_weights, dim=-1)
            if dropout_p > 0.0:
                attn_output_weights = dropout(attn_output_weights, p=dropout_p)

            attn_output = torch.bmm(attn_output_weights, v)

            attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
            attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
            attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))

            # optionally average attention weights over heads
            attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
            if average_attn_weights:
                attn_output_weights = attn_output_weights.mean(dim=1)

            if not is_batched:
                # squeeze the output if input was unbatched
                attn_output = attn_output.squeeze(1)
                attn_output_weights = attn_output_weights.squeeze(0)
            return attn_output, attn_output_weights
        else:
            # attn_mask can be either (L,S) or (N*num_heads, L, S)
            # if attn_mask's shape is (1, L, S) we need to unsqueeze to (1, 1, L, S)
            # in order to match the input for SDPA of (N, num_heads, L, S)
            if attn_mask is not None:
                if attn_mask.size(0) == 1 and attn_mask.dim() == 3:
                    attn_mask = attn_mask.unsqueeze(0)
                else:
                    attn_mask = attn_mask.view(bsz, num_heads, -1, src_len)

            q = q.view(bsz, num_heads, tgt_len, head_dim)
            k = k.view(bsz, num_heads, src_len, head_dim)
            v = v.view(bsz, num_heads, src_len, head_dim)

            attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
            attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)

            attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
            attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
            if not is_batched:
                # squeeze the output if input was unbatched
                attn_output = attn_output.squeeze(1)
            return attn_output, None

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.