conv.py

# mypy: allow-untyped-defs
import math
from typing import List, Optional, Tuple, Union
from typing_extensions import deprecated

import torch
from torch import Tensor
from torch._torch_docs import reproducibility_notes
from torch.nn import functional as F, init
from torch.nn.common_types import _size_1_t, _size_2_t, _size_3_t
from torch.nn.parameter import Parameter, UninitializedParameter

from .lazy import LazyModuleMixin
from .module import Module
from .utils import _pair, _reverse_repeat_tuple, _single, _triple


__all__ = [
    "Conv1d",
    "Conv2d",
    "Conv3d",
    "ConvTranspose1d",
    "ConvTranspose2d",
    "ConvTranspose3d",
    "LazyConv1d",
    "LazyConv2d",
    "LazyConv3d",
    "LazyConvTranspose1d",
    "LazyConvTranspose2d",
    "LazyConvTranspose3d",
]

convolution_notes = {
    "groups_note": r"""* :attr:`groups` controls the connections between inputs and outputs.
      :attr:`in_channels` and :attr:`out_channels` must both be divisible by
      :attr:`groups`. For example,

        * At groups=1, all inputs are convolved to all outputs.
        * At groups=2, the operation becomes equivalent to having two conv
          layers side by side, each seeing half the input channels
          and producing half the output channels, and both subsequently
          concatenated.
        * At groups= :attr:`in_channels`, each input channel is convolved with
          its own set of filters (of size
          :math:`\frac{\text{out\_channels}}{\text{in\_channels}}`).""",
    "depthwise_separable_note": r"""When `groups == in_channels` and `out_channels == K * in_channels`,
        where `K` is a positive integer, this operation is also known as a "depthwise convolution".

        In other words, for an input of size :math:`(N, C_{in}, L_{in})`,
        a depthwise convolution with a depthwise multiplier `K` can be performed with the arguments
        :math:`(C_\text{in}=C_\text{in}, C_\text{out}=C_\text{in} \times \text{K}, ..., \text{groups}=C_\text{in})`.""",
}  # noqa: B950


class _ConvNd(Module):
    __constants__ = [
        "stride",
        "padding",
        "dilation",
        "groups",
        "padding_mode",
        "output_padding",
        "in_channels",
        "out_channels",
        "kernel_size",
    ]
    __annotations__ = {"bias": Optional[torch.Tensor]}

    def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:  # type: ignore[empty-body]
        ...

    in_channels: int
    _reversed_padding_repeated_twice: List[int]
    out_channels: int
    kernel_size: Tuple[int, ...]
    stride: Tuple[int, ...]
    padding: Union[str, Tuple[int, ...]]
    dilation: Tuple[int, ...]
    transposed: bool
    output_padding: Tuple[int, ...]
    groups: int
    padding_mode: str
    weight: Tensor
    bias: Optional[Tensor]

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Tuple[int, ...],
        stride: Tuple[int, ...],
        padding: Tuple[int, ...],
        dilation: Tuple[int, ...],
        transposed: bool,
        output_padding: Tuple[int, ...],
        groups: int,
        bias: bool,
        padding_mode: str,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        if groups <= 0:
            raise ValueError("groups must be a positive integer")
        if in_channels % groups != 0:
            raise ValueError("in_channels must be divisible by groups")
        if out_channels % groups != 0:
            raise ValueError("out_channels must be divisible by groups")
        valid_padding_strings = {"same", "valid"}
        if isinstance(padding, str):
            if padding not in valid_padding_strings:
                raise ValueError(
                    f"Invalid padding string {padding!r}, should be one of {valid_padding_strings}"
                )
            if padding == "same" and any(s != 1 for s in stride):
                raise ValueError(
                    "padding='same' is not supported for strided convolutions"
                )

        valid_padding_modes = {"zeros", "reflect", "replicate", "circular"}
        if padding_mode not in valid_padding_modes:
            raise ValueError(
                f"padding_mode must be one of {valid_padding_modes}, but got padding_mode='{padding_mode}'"
            )
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        self.padding_mode = padding_mode
        # `_reversed_padding_repeated_twice` is the padding to be passed to
        # `F.pad` if needed (e.g., for non-zero padding types that are
        # implemented as two ops: padding + conv). `F.pad` accepts paddings in
        # reverse order than the dimension.
        if isinstance(self.padding, str):
            self._reversed_padding_repeated_twice = [0, 0] * len(kernel_size)
            if padding == "same":
                for d, k, i in zip(
                    dilation, kernel_size, range(len(kernel_size) - 1, -1, -1)
                ):
                    total_padding = d * (k - 1)
                    left_pad = total_padding // 2
                    self._reversed_padding_repeated_twice[2 * i] = left_pad
                    self._reversed_padding_repeated_twice[2 * i + 1] = (
                        total_padding - left_pad
                    )
        else:
            self._reversed_padding_repeated_twice = _reverse_repeat_tuple(
                self.padding, 2
            )

        if transposed:
            self.weight = Parameter(
                torch.empty(
                    (in_channels, out_channels // groups, *kernel_size),
                    **factory_kwargs,
                )
            )
        else:
            self.weight = Parameter(
                torch.empty(
                    (out_channels, in_channels // groups, *kernel_size),
                    **factory_kwargs,
                )
            )
        if bias:
            self.bias = Parameter(torch.empty(out_channels, **factory_kwargs))
        else:
            self.register_parameter("bias", None)

        self.reset_parameters()

    def reset_parameters(self) -> None:
        # Setting a=sqrt(5) in kaiming_uniform is the same as initializing with
        # uniform(-1/sqrt(k), 1/sqrt(k)), where k = weight.size(1) * prod(*kernel_size)
        # For more details see: https://github.com/pytorch/pytorch/issues/15314#issuecomment-477448573
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
            if fan_in != 0:
                bound = 1 / math.sqrt(fan_in)
                init.uniform_(self.bias, -bound, bound)

    def extra_repr(self):
        s = (
            "{in_channels}, {out_channels}, kernel_size={kernel_size}"
            ", stride={stride}"
        )
        if self.padding != (0,) * len(self.padding):
            s += ", padding={padding}"
        if self.dilation != (1,) * len(self.dilation):
            s += ", dilation={dilation}"
        if self.output_padding != (0,) * len(self.output_padding):
            s += ", output_padding={output_padding}"
        if self.groups != 1:
            s += ", groups={groups}"
        if self.bias is None:
            s += ", bias=False"
        if self.padding_mode != "zeros":
            s += ", padding_mode={padding_mode}"
        return s.format(**self.__dict__)

    def __setstate__(self, state):
        super().__setstate__(state)
        if not hasattr(self, "padding_mode"):
            self.padding_mode = "zeros"


class Conv1d(_ConvNd):
    __doc__ = (
        r"""Applies a 1D convolution over an input signal composed of several input
    planes.

    In the simplest case, the output value of the layer with input size
    :math:`(N, C_{\text{in}}, L)` and output :math:`(N, C_{\text{out}}, L_{\text{out}})` can be
    precisely described as:

    .. math::
        \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
        \sum_{k = 0}^{C_{in} - 1} \text{weight}(C_{\text{out}_j}, k)
        \star \text{input}(N_i, k)

    where :math:`\star` is the valid `cross-correlation`_ operator,
    :math:`N` is a batch size, :math:`C` denotes a number of channels,
    :math:`L` is a length of signal sequence.
    """
        + r"""

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

    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

    * :attr:`stride` controls the stride for the cross-correlation, a single
      number or a one-element tuple.

    * :attr:`padding` controls the amount of padding applied to the input. It
      can be either a string {{'valid', 'same'}} or a tuple of ints giving the
      amount of implicit padding applied on both sides.

    * :attr:`dilation` controls the spacing between the kernel points; also
      known as the \uue0 trous algorithm. It is harder to describe, but this `link`_
      has a nice visualization of what :attr:`dilation` does.

    {groups_note}

    Note:
        {depthwise_separable_note}
    Note:
        {cudnn_reproducibility_note}

    Note:
        ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
        the input so the output has the shape as the input. However, this mode
        doesn't support any stride values other than 1.

    Note:
        This module supports complex data types i.e. ``complex32, complex64, complex128``.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int, tuple or str, optional): Padding added to both sides of
            the input. Default: 0
        padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
        dilation (int or tuple, optional): Spacing between kernel
            elements. Default: 1
        groups (int, optional): Number of blocked connections from input
            channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the
            output. Default: ``True``

    """.format(
            **reproducibility_notes, **convolution_notes
        )
        + r"""

    Shape:
        - Input: :math:`(N, C_{in}, L_{in})` or :math:`(C_{in}, L_{in})`
        - Output: :math:`(N, C_{out}, L_{out})` or :math:`(C_{out}, L_{out})`, where

          .. math::
              L_{out} = \left\lfloor\frac{L_{in} + 2 \times \text{padding} - \text{dilation}
                        \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
            :math:`(\text{out\_channels},
            \frac{\text{in\_channels}}{\text{groups}}, \text{kernel\_size})`.
            The values of these weights are sampled from
            :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}`
        bias (Tensor):   the learnable bias of the module of shape
            (out_channels). If :attr:`bias` is ``True``, then the values of these weights are
            sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}`

    Examples::

        >>> m = nn.Conv1d(16, 33, 3, stride=2)
        >>> input = torch.randn(20, 16, 50)
        >>> output = m(input)

    .. _cross-correlation:
        https://en.wikipedia.org/wiki/Cross-correlation

    .. _link:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
    """
    )

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: _size_1_t,
        stride: _size_1_t = 1,
        padding: Union[str, _size_1_t] = 0,
        dilation: _size_1_t = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = "zeros",  # TODO: refine this type
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        # we create new variables below to make mypy happy since kernel_size has
        # type Union[int, Tuple[int]] and kernel_size_ has type Tuple[int]
        kernel_size_ = _single(kernel_size)
        stride_ = _single(stride)
        padding_ = padding if isinstance(padding, str) else _single(padding)
        dilation_ = _single(dilation)
        super().__init__(
            in_channels,
            out_channels,
            kernel_size_,
            stride_,
            padding_,
            dilation_,
            False,
            _single(0),
            groups,
            bias,
            padding_mode,
            **factory_kwargs,
        )

    def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
        if self.padding_mode != "zeros":
            return F.conv1d(
                F.pad(
                    input, self._reversed_padding_repeated_twice, mode=self.padding_mode
                ),
                weight,
                bias,
                self.stride,
                _single(0),
                self.dilation,
                self.groups,
            )
        return F.conv1d(
            input, weight, bias, self.stride, self.padding, self.dilation, self.groups
        )

    def forward(self, input: Tensor) -> Tensor:
        return self._conv_forward(input, self.weight, self.bias)


class Conv2d(_ConvNd):
    __doc__ = (
        r"""Applies a 2D convolution over an input signal composed of several input
    planes.

    In the simplest case, the output value of the layer with input size
    :math:`(N, C_{\text{in}}, H, W)` and output :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})`
    can be precisely described as:

    .. math::
        \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
        \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)


    where :math:`\star` is the valid 2D `cross-correlation`_ operator,
    :math:`N` is a batch size, :math:`C` denotes a number of channels,
    :math:`H` is a height of input planes in pixels, and :math:`W` is
    width in pixels.
    """
        + r"""

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

    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

    * :attr:`stride` controls the stride for the cross-correlation, a single
      number or a tuple.

    * :attr:`padding` controls the amount of padding applied to the input. It
      can be either a string {{'valid', 'same'}} or an int / a tuple of ints giving the
      amount of implicit padding applied on both sides.

    * :attr:`dilation` controls the spacing between the kernel points; also
      known as the \u00e0 trous algorithm. It is harder to describe, but this `link`_
      has a nice visualization of what :attr:`dilation` does.

    {groups_note}

    The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:

        - a single ``int`` -- in which case the same value is used for the height and width dimension
        - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension,
          and the second `int` for the width dimension

    Note:
        {depthwise_separable_note}

    Note:
        {cudnn_reproducibility_note}

    Note:
        ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
        the input so the output has the shape as the input. However, this mode
        doesn't support any stride values other than 1.

    Note:
        This module supports complex data types i.e. ``complex32, complex64, complex128``.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int, tuple or str, optional): Padding added to all four sides of
            the input. Default: 0
        padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
        groups (int, optional): Number of blocked connections from input
            channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the
            output. Default: ``True``
    """.format(
            **reproducibility_notes, **convolution_notes
        )
        + r"""

    Shape:
        - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(C_{out}, H_{out}, W_{out})`, where

          .. math::
              H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \text{padding}[0] - \text{dilation}[0]
                        \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor

          .. math::
              W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \text{padding}[1] - \text{dilation}[1]
                        \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
            :math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`
            :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`.
            The values of these weights are sampled from
            :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
        bias (Tensor):   the learnable bias of the module of shape
            (out_channels). If :attr:`bias` is ``True``,
            then the values of these weights are
            sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`

    Examples:

        >>> # With square kernels and equal stride
        >>> m = nn.Conv2d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
        >>> # non-square kernels and unequal stride and with padding and dilation
        >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
        >>> input = torch.randn(20, 16, 50, 100)
        >>> output = m(input)

    .. _cross-correlation:
        https://en.wikipedia.org/wiki/Cross-correlation

    .. _link:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
    """
    )

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: _size_2_t,
        stride: _size_2_t = 1,
        padding: Union[str, _size_2_t] = 0,
        dilation: _size_2_t = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = "zeros",  # TODO: refine this type
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        kernel_size_ = _pair(kernel_size)
        stride_ = _pair(stride)
        padding_ = padding if isinstance(padding, str) else _pair(padding)
        dilation_ = _pair(dilation)
        super().__init__(
            in_channels,
            out_channels,
            kernel_size_,
            stride_,
            padding_,
            dilation_,
            False,
            _pair(0),
            groups,
            bias,
            padding_mode,
            **factory_kwargs,
        )

    def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
        if self.padding_mode != "zeros":
            return F.conv2d(
                F.pad(
                    input, self._reversed_padding_repeated_twice, mode=self.padding_mode
                ),
                weight,
                bias,
                self.stride,
                _pair(0),
                self.dilation,
                self.groups,
            )
        return F.conv2d(
            input, weight, bias, self.stride, self.padding, self.dilation, self.groups
        )

    def forward(self, input: Tensor) -> Tensor:
        return self._conv_forward(input, self.weight, self.bias)


class Conv3d(_ConvNd):
    __doc__ = (
        r"""Applies a 3D convolution over an input signal composed of several input
    planes.

    In the simplest case, the output value of the layer with input size :math:`(N, C_{in}, D, H, W)`
    and output :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` can be precisely described as:

    .. math::
        out(N_i, C_{out_j}) = bias(C_{out_j}) +
                                \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k)

    where :math:`\star` is the valid 3D `cross-correlation`_ operator
    """
        + r"""

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

    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

    * :attr:`stride` controls the stride for the cross-correlation.

    * :attr:`padding` controls the amount of padding applied to the input. It
      can be either a string {{'valid', 'same'}} or a tuple of ints giving the
      amount of implicit padding applied on both sides.

    * :attr:`dilation` controls the spacing between the kernel points; also known as the \u00e0 trous algorithm.
      It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does.

    {groups_note}

    The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:

        - a single ``int`` -- in which case the same value is used for the depth, height and width dimension
        - a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension,
          the second `int` for the height dimension and the third `int` for the width dimension

    Note:
        {depthwise_separable_note}

    Note:
        {cudnn_reproducibility_note}

    Note:
        ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
        the input so the output has the shape as the input. However, this mode
        doesn't support any stride values other than 1.

    Note:
        This module supports complex data types i.e. ``complex32, complex64, complex128``.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int, tuple or str, optional): Padding added to all six sides of
            the input. Default: 0
        padding_mode (str, optional): ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
    """.format(
            **reproducibility_notes, **convolution_notes
        )
        + r"""

    Shape:
        - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` or :math:`(C_{in}, D_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` or :math:`(C_{out}, D_{out}, H_{out}, W_{out})`,
          where

          .. math::
              D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0]
                    \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor

          .. math::
              H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1]
                    \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor

          .. math::
              W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2]
                    \times (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
                         :math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`
                         :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]})`.
                         The values of these weights are sampled from
                         :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                         :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`
        bias (Tensor):   the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``,
                         then the values of these weights are
                         sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                         :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`

    Examples::

        >>> # With square kernels and equal stride
        >>> m = nn.Conv3d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.Conv3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(4, 2, 0))
        >>> input = torch.randn(20, 16, 10, 50, 100)
        >>> output = m(input)

    .. _cross-correlation:
        https://en.wikipedia.org/wiki/Cross-correlation

    .. _link:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
    """
    )

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: _size_3_t,
        stride: _size_3_t = 1,
        padding: Union[str, _size_3_t] = 0,
        dilation: _size_3_t = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        kernel_size_ = _triple(kernel_size)
        stride_ = _triple(stride)
        padding_ = padding if isinstance(padding, str) else _triple(padding)
        dilation_ = _triple(dilation)
        super().__init__(
            in_channels,
            out_channels,
            kernel_size_,
            stride_,
            padding_,
            dilation_,
            False,
            _triple(0),
            groups,
            bias,
            padding_mode,
            **factory_kwargs,
        )

    def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
        if self.padding_mode != "zeros":
            return F.conv3d(
                F.pad(
                    input, self._reversed_padding_repeated_twice, mode=self.padding_mode
                ),
                weight,
                bias,
                self.stride,
                _triple(0),
                self.dilation,
                self.groups,
            )
        return F.conv3d(
            input, weight, bias, self.stride, self.padding, self.dilation, self.groups
        )

    def forward(self, input: Tensor) -> Tensor:
        return self._conv_forward(input, self.weight, self.bias)


class _ConvTransposeNd(_ConvNd):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        transposed,
        output_padding,
        groups,
        bias,
        padding_mode,
        device=None,
        dtype=None,
    ) -> None:
        if padding_mode != "zeros":
            raise ValueError(
                f'Only "zeros" padding mode is supported for {self.__class__.__name__}'
            )

        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            transposed,
            output_padding,
            groups,
            bias,
            padding_mode,
            **factory_kwargs,
        )

    # dilation being an optional parameter is for backwards
    # compatibility
    def _output_padding(
        self,
        input: Tensor,
        output_size: Optional[List[int]],
        stride: List[int],
        padding: List[int],
        kernel_size: List[int],
        num_spatial_dims: int,
        dilation: Optional[List[int]] = None,
    ) -> List[int]:
        if output_size is None:
            ret = _single(self.output_padding)  # converting to list if was not already
        else:
            has_batch_dim = input.dim() == num_spatial_dims + 2
            num_non_spatial_dims = 2 if has_batch_dim else 1
            if len(output_size) == num_non_spatial_dims + num_spatial_dims:
                output_size = output_size[num_non_spatial_dims:]
            if len(output_size) != num_spatial_dims:
                raise ValueError(
                    f"ConvTranspose{num_spatial_dims}D: for {input.dim()}D input, output_size must have {num_spatial_dims} "
                    f"or {num_non_spatial_dims + num_spatial_dims} elements (got {len(output_size)})"
                )

            min_sizes = torch.jit.annotate(List[int], [])
            max_sizes = torch.jit.annotate(List[int], [])
            for d in range(num_spatial_dims):
                dim_size = (
                    (input.size(d + num_non_spatial_dims) - 1) * stride[d]
                    - 2 * padding[d]
                    + (dilation[d] if dilation is not None else 1)
                    * (kernel_size[d] - 1)
                    + 1
                )
                min_sizes.append(dim_size)
                max_sizes.append(min_sizes[d] + stride[d] - 1)

            for i in range(len(output_size)):
                size = output_size[i]
                min_size = min_sizes[i]
                max_size = max_sizes[i]
                if size < min_size or size > max_size:
                    raise ValueError(
                        f"requested an output size of {output_size}, but valid sizes range "
                        f"from {min_sizes} to {max_sizes} (for an input of {input.size()[2:]})"
                    )

            res = torch.jit.annotate(List[int], [])
            for d in range(num_spatial_dims):
                res.append(output_size[d] - min_sizes[d])

            ret = res
        return ret


class ConvTranspose1d(_ConvTransposeNd):
    __doc__ = (
        r"""Applies a 1D transposed convolution operator over an input image
    composed of several input planes.

    This module can be seen as the gradient of Conv1d with respect to its input.
    It is also known as a fractionally-strided convolution or
    a deconvolution (although it is not an actual deconvolution operation as it does
    not compute a true inverse of convolution). For more information, see the visualizations
    `here`_ and the `Deconvolutional Networks`_ paper.

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

    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

    * :attr:`stride` controls the stride for the cross-correlation.

    * :attr:`padding` controls the amount of implicit zero padding on both
      sides for ``dilation * (kernel_size - 1) - padding`` number of points. See note
      below for details.

    * :attr:`output_padding` controls the additional size added to one side
      of the output shape. See note below for details.

    * :attr:`dilation` controls the spacing between the kernel points; also known as the \u00e0 trous algorithm.
      It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does.

    {groups_note}

    Note:
        The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding``
        amount of zero padding to both sizes of the input. This is set so that
        when a :class:`~torch.nn.Conv1d` and a :class:`~torch.nn.ConvTranspose1d`
        are initialized with same parameters, they are inverses of each other in
        regard to the input and output shapes. However, when ``stride > 1``,
        :class:`~torch.nn.Conv1d` maps multiple input shapes to the same output
        shape. :attr:`output_padding` is provided to resolve this ambiguity by
        effectively increasing the calculated output shape on one side. Note
        that :attr:`output_padding` is only used to find output shape, but does
        not actually add zero-padding to output.

    Note:
        In some circumstances when using the CUDA backend with CuDNN, this operator
        may select a nondeterministic algorithm to increase performance. If this is
        undesirable, you can try to make the operation deterministic (potentially at
        a performance cost) by setting ``torch.backends.cudnn.deterministic =
        True``.
        Please see the notes on :doc:`/notes/randomness` for background.


    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding
            will be added to both sides of the input. Default: 0
        output_padding (int or tuple, optional): Additional size added to one side
            of the output shape. Default: 0
        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
    """.format(
            **reproducibility_notes, **convolution_notes
        )
        + r"""

    Shape:
        - Input: :math:`(N, C_{in}, L_{in})` or :math:`(C_{in}, L_{in})`
        - Output: :math:`(N, C_{out}, L_{out})` or :math:`(C_{out}, L_{out})`, where

          .. math::
              L_{out} = (L_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{dilation}
                        \times (\text{kernel\_size} - 1) + \text{output\_padding} + 1

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
                         :math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},`
                         :math:`\text{kernel\_size})`.
                         The values of these weights are sampled from
                         :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                         :math:`k = \frac{groups}{C_\text{out} * \text{kernel\_size}}`
        bias (Tensor):   the learnable bias of the module of shape (out_channels).
                         If :attr:`bias` is ``True``, then the values of these weights are
                         sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                         :math:`k = \frac{groups}{C_\text{out} * \text{kernel\_size}}`

    .. _`here`:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

    .. _`Deconvolutional Networks`:
        https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf
    """
    )

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: _size_1_t,
        stride: _size_1_t = 1,
        padding: _size_1_t = 0,
        output_padding: _size_1_t = 0,
        groups: int = 1,
        bias: bool = True,
        dilation: _size_1_t = 1,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        kernel_size = _single(kernel_size)
        stride = _single(stride)
        padding = _single(padding)
        dilation = _single(dilation)
        output_padding = _single(output_padding)
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            True,
            output_padding,
            groups,
            bias,
            padding_mode,
            **factory_kwargs,
        )

    def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
        if self.padding_mode != "zeros":
            raise ValueError(
                "Only `zeros` padding mode is supported for ConvTranspose1d"
            )

        assert isinstance(self.padding, tuple)
        # One cannot replace List by Tuple or Sequence in "_output_padding" because
        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
        num_spatial_dims = 1
        output_padding = self._output_padding(
            input,
            output_size,
            self.stride,  # type: ignore[arg-type]
            self.padding,  # type: ignore[arg-type]
            self.kernel_size,  # type: ignore[arg-type]
            num_spatial_dims,
            self.dilation,  # type: ignore[arg-type]
        )
        return F.conv_transpose1d(
            input,
            self.weight,
            self.bias,
            self.stride,
            self.padding,
            output_padding,
            self.groups,
            self.dilation,
        )


class ConvTranspose2d(_ConvTransposeNd):
    __doc__ = (
        r"""Applies a 2D transposed convolution operator over an input image
    composed of several input planes.

    This module can be seen as the gradient of Conv2d with respect to its input.
    It is also known as a fractionally-strided convolution or
    a deconvolution (although it is not an actual deconvolution operation as it does
    not compute a true inverse of convolution). For more information, see the visualizations
    `here`_ and the `Deconvolutional Networks`_ paper.

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

    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

    * :attr:`stride` controls the stride for the cross-correlation.

    * :attr:`padding` controls the amount of implicit zero padding on both
      sides for ``dilation * (kernel_size - 1) - padding`` number of points. See note
      below for details.

    * :attr:`output_padding` controls the additional size added to one side
      of the output shape. See note below for details.

    * :attr:`dilation` controls the spacing between the kernel points; also known as the \u00e0 trous algorithm.
      It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does.

    {groups_note}

    The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding`
    can either be:

        - a single ``int`` -- in which case the same value is used for the height and width dimensions
        - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension,
          and the second `int` for the width dimension

    Note:
        The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding``
        amount of zero padding to both sizes of the input. This is set so that
        when a :class:`~torch.nn.Conv2d` and a :class:`~torch.nn.ConvTranspose2d`
        are initialized with same parameters, they are inverses of each other in
        regard to the input and output shapes. However, when ``stride > 1``,
        :class:`~torch.nn.Conv2d` maps multiple input shapes to the same output
        shape. :attr:`output_padding` is provided to resolve this ambiguity by
        effectively increasing the calculated output shape on one side. Note
        that :attr:`output_padding` is only used to find output shape, but does
        not actually add zero-padding to output.

    Note:
        {cudnn_reproducibility_note}

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding
            will be added to both sides of each dimension in the input. Default: 0
        output_padding (int or tuple, optional): Additional size added to one side
            of each dimension in the output shape. Default: 0
        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
    """.format(
            **reproducibility_notes, **convolution_notes
        )
        + r"""

    Shape:
        - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(C_{out}, H_{out}, W_{out})`, where

        .. math::
              H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0]
                        \times (\text{kernel\_size}[0] - 1) + \text{output\_padding}[0] + 1
        .. math::
              W_{out} = (W_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1]
                        \times (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
                         :math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},`
                         :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`.
                         The values of these weights are sampled from
                         :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                         :math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
        bias (Tensor):   the learnable bias of the module of shape (out_channels)
                         If :attr:`bias` is ``True``, then the values of these weights are
                         sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                         :math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`

    Examples::

        >>> # With square kernels and equal stride
        >>> m = nn.ConvTranspose2d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
        >>> input = torch.randn(20, 16, 50, 100)
        >>> output = m(input)
        >>> # exact output size can be also specified as an argument
        >>> input = torch.randn(1, 16, 12, 12)
        >>> downsample = nn.Conv2d(16, 16, 3, stride=2, padding=1)
        >>> upsample = nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
        >>> h = downsample(input)
        >>> h.size()
        torch.Size([1, 16, 6, 6])
        >>> output = upsample(h, output_size=input.size())
        >>> output.size()
        torch.Size([1, 16, 12, 12])

    .. _`here`:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

    .. _`Deconvolutional Networks`:
        https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf
    """
    )

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: _size_2_t,
        stride: _size_2_t = 1,
        padding: _size_2_t = 0,
        output_padding: _size_2_t = 0,
        groups: int = 1,
        bias: bool = True,
        dilation: _size_2_t = 1,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        output_padding = _pair(output_padding)
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            True,
            output_padding,
            groups,
            bias,
            padding_mode,
            **factory_kwargs,
        )

    def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
        if self.padding_mode != "zeros":
            raise ValueError(
                "Only `zeros` padding mode is supported for ConvTranspose2d"
            )

        assert isinstance(self.padding, tuple)
        # One cannot replace List by Tuple or Sequence in "_output_padding" because
        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
        num_spatial_dims = 2
        output_padding = self._output_padding(
            input,
            output_size,
            self.stride,  # type: ignore[arg-type]
            self.padding,  # type: ignore[arg-type]
            self.kernel_size,  # type: ignore[arg-type]
            num_spatial_dims,
            self.dilation,  # type: ignore[arg-type]
        )

        return F.conv_transpose2d(
            input,
            self.weight,
            self.bias,
            self.stride,
            self.padding,
            output_padding,
            self.groups,
            self.dilation,
        )


class ConvTranspose3d(_ConvTransposeNd):
    __doc__ = (
        r"""Applies a 3D transposed convolution operator over an input image composed of several input
    planes.
    The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
    and sums over the outputs from all input feature planes.

    This module can be seen as the gradient of Conv3d with respect to its input.
    It is also known as a fractionally-strided convolution or
    a deconvolution (although it is not an actual deconvolution operation as it does
    not compute a true inverse of convolution). For more information, see the visualizations
    `here`_ and the `Deconvolutional Networks`_ paper.

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

    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

    * :attr:`stride` controls the stride for the cross-correlation.

    * :attr:`padding` controls the amount of implicit zero padding on both
      sides for ``dilation * (kernel_size - 1) - padding`` number of points. See note
      below for details.

    * :attr:`output_padding` controls the additional size added to one side
      of the output shape. See note below for details.

    * :attr:`dilation` controls the spacing between the kernel points; also known as the \u00e0 trous algorithm.
      It is harder to describe, but the link `here`_ has a nice visualization of what :attr:`dilation` does.

    {groups_note}

    The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`output_padding`
    can either be:

        - a single ``int`` -- in which case the same value is used for the depth, height and width dimensions
        - a ``tuple`` of three ints -- in which case, the first `int` is used for the depth dimension,
          the second `int` for the height dimension and the third `int` for the width dimension

    Note:
        The :attr:`padding` argument effectively adds ``dilation * (kernel_size - 1) - padding``
        amount of zero padding to both sizes of the input. This is set so that
        when a :class:`~torch.nn.Conv3d` and a :class:`~torch.nn.ConvTranspose3d`
        are initialized with same parameters, they are inverses of each other in
        regard to the input and output shapes. However, when ``stride > 1``,
        :class:`~torch.nn.Conv3d` maps multiple input shapes to the same output
        shape. :attr:`output_padding` is provided to resolve this ambiguity by
        effectively increasing the calculated output shape on one side. Note
        that :attr:`output_padding` is only used to find output shape, but does
        not actually add zero-padding to output.

    Note:
        {cudnn_reproducibility_note}

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding
            will be added to both sides of each dimension in the input. Default: 0
        output_padding (int or tuple, optional): Additional size added to one side
            of each dimension in the output shape. Default: 0
        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
    """.format(
            **reproducibility_notes, **convolution_notes
        )
        + r"""

    Shape:
        - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` or :math:`(C_{in}, D_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` or
          :math:`(C_{out}, D_{out}, H_{out}, W_{out})`, where

        .. math::
              D_{out} = (D_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0]
                        \times (\text{kernel\_size}[0] - 1) + \text{output\_padding}[0] + 1
        .. math::
              H_{out} = (H_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1]
                        \times (\text{kernel\_size}[1] - 1) + \text{output\_padding}[1] + 1
        .. math::
              W_{out} = (W_{in} - 1) \times \text{stride}[2] - 2 \times \text{padding}[2] + \text{dilation}[2]
                        \times (\text{kernel\_size}[2] - 1) + \text{output\_padding}[2] + 1


    Attributes:
        weight (Tensor): the learnable weights of the module of shape
                         :math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},`
                         :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]})`.
                         The values of these weights are sampled from
                         :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                         :math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`
        bias (Tensor):   the learnable bias of the module of shape (out_channels)
                         If :attr:`bias` is ``True``, then the values of these weights are
                         sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                         :math:`k = \frac{groups}{C_\text{out} * \prod_{i=0}^{2}\text{kernel\_size}[i]}`

    Examples::

        >>> # With square kernels and equal stride
        >>> m = nn.ConvTranspose3d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.ConvTranspose3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2))
        >>> input = torch.randn(20, 16, 10, 50, 100)
        >>> output = m(input)

    .. _`here`:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

    .. _`Deconvolutional Networks`:
        https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf
    """
    )

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: _size_3_t,
        stride: _size_3_t = 1,
        padding: _size_3_t = 0,
        output_padding: _size_3_t = 0,
        groups: int = 1,
        bias: bool = True,
        dilation: _size_3_t = 1,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        kernel_size = _triple(kernel_size)
        stride = _triple(stride)
        padding = _triple(padding)
        dilation = _triple(dilation)
        output_padding = _triple(output_padding)
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            True,
            output_padding,
            groups,
            bias,
            padding_mode,
            **factory_kwargs,
        )

    def forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
        if self.padding_mode != "zeros":
            raise ValueError(
                "Only `zeros` padding mode is supported for ConvTranspose3d"
            )

        assert isinstance(self.padding, tuple)
        # One cannot replace List by Tuple or Sequence in "_output_padding" because
        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
        num_spatial_dims = 3
        output_padding = self._output_padding(
            input,
            output_size,
            self.stride,  # type: ignore[arg-type]
            self.padding,  # type: ignore[arg-type]
            self.kernel_size,  # type: ignore[arg-type]
            num_spatial_dims,
            self.dilation,  # type: ignore[arg-type]
        )

        return F.conv_transpose3d(
            input,
            self.weight,
            self.bias,
            self.stride,
            self.padding,
            output_padding,
            self.groups,
            self.dilation,
        )


# TODO: Deprecate and remove the following alias `_ConvTransposeMixin`.
#
# `_ConvTransposeMixin` was a mixin that was removed.  It is meant to be used
# with `_ConvNd` to construct actual module classes that implements conv
# transpose ops:
#
#   class MyConvTranspose(_ConvNd, _ConvTransposeMixin):
#       ...
#
# In PyTorch, it has been replaced by `_ConvTransposeNd`, which is a proper
# subclass of `_ConvNd`.  However, some user code in the wild still (incorrectly)
# use the internal class `_ConvTransposeMixin`.  Hence, we provide this alias
# for BC, because it is cheap and easy for us to do so, even though that
# `_ConvTransposeNd` is really not a mixin anymore (but multiple inheritance as
# above would still work).
class _ConvTransposeMixin(_ConvTransposeNd):
    @deprecated(
        "`_ConvTransposeMixin` is a deprecated internal class. "
        "Please consider using public APIs.",
        category=FutureWarning,
    )
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)


# TODO: Conv2dLocal
# TODO: Conv2dMap
# TODO: ConvTranspose2dMap


class _LazyConvXdMixin(LazyModuleMixin):
    groups: int
    transposed: bool
    in_channels: int
    out_channels: int
    kernel_size: Tuple[int, ...]
    weight: UninitializedParameter
    bias: UninitializedParameter

    def reset_parameters(self) -> None:
        # has_uninitialized_params is defined in parent class and it is using a protocol on self
        if not self.has_uninitialized_params() and self.in_channels != 0:  # type: ignore[misc]
            # "type:ignore[..]" is required because mypy thinks that "reset_parameters" is undefined
            # in super class. Turns out that it is defined in _ConvND which is inherited by any class
            # that also inherits _LazyConvXdMixin
            super().reset_parameters()  # type: ignore[misc]

    # Signature of "initialize_parameters" is incompatible with the definition in supertype LazyModuleMixin
    def initialize_parameters(self, input: Tensor, *args, **kwargs) -> None:  # type: ignore[override]
        # defined by parent class but using a protocol
        if self.has_uninitialized_params():  # type: ignore[misc]
            self.in_channels = self._get_in_channels(input)
            if self.in_channels % self.groups != 0:
                raise ValueError("in_channels must be divisible by groups")
            assert isinstance(self.weight, UninitializedParameter)
            if self.transposed:
                self.weight.materialize(
                    (
                        self.in_channels,
                        self.out_channels // self.groups,
                        *self.kernel_size,
                    )
                )
            else:
                self.weight.materialize(
                    (
                        self.out_channels,
                        self.in_channels // self.groups,
                        *self.kernel_size,
                    )
                )
            if self.bias is not None:
                assert isinstance(self.bias, UninitializedParameter)
                self.bias.materialize((self.out_channels,))
            self.reset_parameters()

    # Function to extract in_channels from first input.
    def _get_in_channels(self, input: Tensor) -> int:
        num_spatial_dims = self._get_num_spatial_dims()
        num_dims_no_batch = num_spatial_dims + 1  # +1 for channels dim
        num_dims_batch = num_dims_no_batch + 1
        if input.dim() not in (num_dims_no_batch, num_dims_batch):
            raise RuntimeError(
                f"Expected {num_dims_no_batch}D (unbatched) or {num_dims_batch}D (batched) input "
                f"to {self.__class__.__name__}, but "
                f"got input of size: {input.shape}"
            )
        return input.shape[1] if input.dim() == num_dims_batch else input.shape[0]

    # Function to return the number of spatial dims expected for inputs to the module.
    # This is expected to be implemented by subclasses.
    def _get_num_spatial_dims(self) -> int:
        raise NotImplementedError


# LazyConv1d defines weight as a Tensor but derived class defines it as UnitializeParameter
class LazyConv1d(_LazyConvXdMixin, Conv1d):  # type: ignore[misc]
    r"""A :class:`torch.nn.Conv1d` module with lazy initialization of the ``in_channels`` argument.

    The ``in_channels`` argument of the :class:`Conv1d` is inferred from the ``input.size(1)``.
    The attributes that will be lazily initialized are `weight` and `bias`.

    Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
    on lazy modules and their limitations.

    Args:
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): Zero-padding added to both sides of
            the input. Default: 0
        padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
        dilation (int or tuple, optional): Spacing between kernel
            elements. Default: 1
        groups (int, optional): Number of blocked connections from input
            channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the
            output. Default: ``True``

    .. seealso:: :class:`torch.nn.Conv1d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
    """

    # super class define this variable as None. "type: ignore[..] is required
    # since we are redefining the variable.
    cls_to_become = Conv1d  # type: ignore[assignment]

    def __init__(
        self,
        out_channels: int,
        kernel_size: _size_1_t,
        stride: _size_1_t = 1,
        padding: _size_1_t = 0,
        dilation: _size_1_t = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__(
            0,
            0,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            # bias is hardcoded to False to avoid creating tensor
            # that will soon be overwritten.
            False,
            padding_mode,
            **factory_kwargs,
        )
        self.weight = UninitializedParameter(**factory_kwargs)
        self.out_channels = out_channels
        if bias:
            self.bias = UninitializedParameter(**factory_kwargs)

    def _get_num_spatial_dims(self) -> int:
        return 1


# LazyConv2d defines weight as a Tensor but derived class defines it as UnitializeParameter
class LazyConv2d(_LazyConvXdMixin, Conv2d):  # type: ignore[misc]
    r"""A :class:`torch.nn.Conv2d` module with lazy initialization of the ``in_channels`` argument.

    The ``in_channels`` argument of the :class:`Conv2d` that is inferred from the ``input.size(1)``.
    The attributes that will be lazily initialized are `weight` and `bias`.

    Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
    on lazy modules and their limitations.

    Args:
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): Zero-padding added to both sides of
            the input. Default: 0
        padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
        dilation (int or tuple, optional): Spacing between kernel
            elements. Default: 1
        groups (int, optional): Number of blocked connections from input
            channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the
            output. Default: ``True``

    .. seealso:: :class:`torch.nn.Conv2d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
    """

    # super class define this variable as None. "type: ignore[..] is required
    # since we are redefining the variable.
    cls_to_become = Conv2d  # type: ignore[assignment]

    def __init__(
        self,
        out_channels: int,
        kernel_size: _size_2_t,
        stride: _size_2_t = 1,
        padding: _size_2_t = 0,
        dilation: _size_2_t = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = "zeros",  # TODO: refine this type
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__(
            0,
            0,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            # bias is hardcoded to False to avoid creating tensor
            # that will soon be overwritten.
            False,
            padding_mode,
            **factory_kwargs,
        )
        self.weight = UninitializedParameter(**factory_kwargs)
        self.out_channels = out_channels
        if bias:
            self.bias = UninitializedParameter(**factory_kwargs)

    def _get_num_spatial_dims(self) -> int:
        return 2


# LazyConv3d defines weight as a Tensor but derived class defines it as UnitializeParameter
class LazyConv3d(_LazyConvXdMixin, Conv3d):  # type: ignore[misc]
    r"""A :class:`torch.nn.Conv3d` module with lazy initialization of the ``in_channels`` argument.

    The ``in_channels`` argument of the :class:`Conv3d` that is inferred from
    the ``input.size(1)``.
    The attributes that will be lazily initialized are `weight` and `bias`.

    Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
    on lazy modules and their limitations.

    Args:
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): Zero-padding added to both sides of
            the input. Default: 0
        padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
        dilation (int or tuple, optional): Spacing between kernel
            elements. Default: 1
        groups (int, optional): Number of blocked connections from input
            channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the
            output. Default: ``True``

    .. seealso:: :class:`torch.nn.Conv3d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
    """

    # super class define this variable as None. "type: ignore[..] is required
    # since we are redefining the variable.
    cls_to_become = Conv3d  # type: ignore[assignment]

    def __init__(
        self,
        out_channels: int,
        kernel_size: _size_3_t,
        stride: _size_3_t = 1,
        padding: _size_3_t = 0,
        dilation: _size_3_t = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__(
            0,
            0,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            # bias is hardcoded to False to avoid creating tensor
            # that will soon be overwritten.
            False,
            padding_mode,
            **factory_kwargs,
        )
        self.weight = UninitializedParameter(**factory_kwargs)
        self.out_channels = out_channels
        if bias:
            self.bias = UninitializedParameter(**factory_kwargs)

    def _get_num_spatial_dims(self) -> int:
        return 3


# LazyConvTranspose1d defines weight as a Tensor but derived class defines it as UnitializeParameter
class LazyConvTranspose1d(_LazyConvXdMixin, ConvTranspose1d):  # type: ignore[misc]
    r"""A :class:`torch.nn.ConvTranspose1d` module with lazy initialization of the ``in_channels`` argument.

    The ``in_channels`` argument of the :class:`ConvTranspose1d` that is inferred from
    the ``input.size(1)``.
    The attributes that will be lazily initialized are `weight` and `bias`.

    Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
    on lazy modules and their limitations.

    Args:
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding
            will be added to both sides of the input. Default: 0
        output_padding (int or tuple, optional): Additional size added to one side
            of the output shape. Default: 0
        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1

    .. seealso:: :class:`torch.nn.ConvTranspose1d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
    """

    # super class define this variable as None. "type: ignore[..] is required
    # since we are redefining the variable.
    cls_to_become = ConvTranspose1d  # type: ignore[assignment]

    def __init__(
        self,
        out_channels: int,
        kernel_size: _size_1_t,
        stride: _size_1_t = 1,
        padding: _size_1_t = 0,
        output_padding: _size_1_t = 0,
        groups: int = 1,
        bias: bool = True,
        dilation: _size_1_t = 1,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__(
            0,
            0,
            kernel_size,
            stride,
            padding,
            output_padding,
            groups,
            # bias is hardcoded to False to avoid creating tensor
            # that will soon be overwritten.
            False,
            dilation,
            padding_mode,
            **factory_kwargs,
        )
        self.weight = UninitializedParameter(**factory_kwargs)
        self.out_channels = out_channels
        if bias:
            self.bias = UninitializedParameter(**factory_kwargs)

    def _get_num_spatial_dims(self) -> int:
        return 1


# LazyConvTranspose2d defines weight as a Tensor but derived class defines it as UnitializeParameter
class LazyConvTranspose2d(_LazyConvXdMixin, ConvTranspose2d):  # type: ignore[misc]
    r"""A :class:`torch.nn.ConvTranspose2d` module with lazy initialization of the ``in_channels`` argument.

    The ``in_channels`` argument of the :class:`ConvTranspose2d` is inferred from
    the ``input.size(1)``.
    The attributes that will be lazily initialized are `weight` and `bias`.

    Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
    on lazy modules and their limitations.

    Args:
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding
            will be added to both sides of each dimension in the input. Default: 0
        output_padding (int or tuple, optional): Additional size added to one side
            of each dimension in the output shape. Default: 0
        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1

    .. seealso:: :class:`torch.nn.ConvTranspose2d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
    """

    # super class define this variable as None. "type: ignore[..] is required
    # since we are redefining the variable.
    cls_to_become = ConvTranspose2d  # type: ignore[assignment]

    def __init__(
        self,
        out_channels: int,
        kernel_size: _size_2_t,
        stride: _size_2_t = 1,
        padding: _size_2_t = 0,
        output_padding: _size_2_t = 0,
        groups: int = 1,
        bias: bool = True,
        dilation: int = 1,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__(
            0,
            0,
            kernel_size,
            stride,
            padding,
            output_padding,
            groups,
            # bias is hardcoded to False to avoid creating tensor
            # that will soon be overwritten.
            False,
            dilation,
            padding_mode,
            **factory_kwargs,
        )
        self.weight = UninitializedParameter(**factory_kwargs)
        self.out_channels = out_channels
        if bias:
            self.bias = UninitializedParameter(**factory_kwargs)

    def _get_num_spatial_dims(self) -> int:
        return 2


# LazyConvTranspose3d defines weight as a Tensor but derived class defines it as UnitializeParameter
class LazyConvTranspose3d(_LazyConvXdMixin, ConvTranspose3d):  # type: ignore[misc]
    r"""A :class:`torch.nn.ConvTranspose3d` module with lazy initialization of the ``in_channels`` argument.

    The ``in_channels`` argument of the :class:`ConvTranspose3d` is inferred from
    the ``input.size(1)``.
    The attributes that will be lazily initialized are `weight` and `bias`.

    Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
    on lazy modules and their limitations.

    Args:
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int or tuple, optional): ``dilation * (kernel_size - 1) - padding`` zero-padding
            will be added to both sides of each dimension in the input. Default: 0
        output_padding (int or tuple, optional): Additional size added to one side
            of each dimension in the output shape. Default: 0
        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1

    .. seealso:: :class:`torch.nn.ConvTranspose3d` and :class:`torch.nn.modules.lazy.LazyModuleMixin`
    """

    # super class define this variable as None. "type: ignore[..] is required
    # since we are redefining the variable.
    cls_to_become = ConvTranspose3d  # type: ignore[assignment]

    def __init__(
        self,
        out_channels: int,
        kernel_size: _size_3_t,
        stride: _size_3_t = 1,
        padding: _size_3_t = 0,
        output_padding: _size_3_t = 0,
        groups: int = 1,
        bias: bool = True,
        dilation: _size_3_t = 1,
        padding_mode: str = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__(
            0,
            0,
            kernel_size,
            stride,
            padding,
            output_padding,
            groups,
            # bias is hardcoded to False to avoid creating tensor
            # that will soon be overwritten.
            False,
            dilation,
            padding_mode,
            **factory_kwargs,
        )
        self.weight = UninitializedParameter(**factory_kwargs)
        self.out_channels = out_channels
        if bias:
            self.bias = UninitializedParameter(**factory_kwargs)

    def _get_num_spatial_dims(self) -> int:
        return 3