text: # coding=utf-8
  import math
  import warnings
  
  import torch
  from torch import Tensor
  from torch.nn.parameter import Parameter, UninitializedParameter
  from .. import functional as F
  from .. import init
  from .lazy import LazyModuleMixin
  from .module import Module
  from .utils import _single, _pair, _triple, _reverse_repeat_tuple
  from torch._torch_docs import reproducibility_notes
  
  from ..common_types import _size_1_t, _size_2_t, _size_3_t
  from typing import Optional, List, Tuple, Union
  
  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:
          ...
  
      _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) -> None:
          super(_ConvNd, self).__init__()
          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(
                      "Invalid padding string {!r}, should be one of {}".format(
                          padding, 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("padding_mode must be one of {}, but got padding_mode='{}'".format(
                  valid_padding_modes, 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))
          else:
              self.weight = Parameter(torch.empty(
                  out_channels, in_channels // groups, *kernel_size))
          if bias:
              self.bias = Parameter(torch.empty(out_channels))
          else:
              self.register_parameter('bias', None)
          self.reset_parameters()
  
      def reset_parameters(self) -> None:
          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)
              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(_ConvNd, self).__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>`.
  
      * :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 à 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.
  
      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 (string, 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})`
          - Output: :math:`(N, 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
      ):
          # 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(Conv1d, self).__init__(
              in_channels, out_channels, kernel_size_, stride_, padding_, dilation_,
              False, _single(0), groups, bias, padding_mode)
  
      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>`.
  
      * :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 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 à 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.
  
      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 (string, 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})`
          - Output: :math:`(N, 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
      ):
          kernel_size_ = _pair(kernel_size)
          stride_ = _pair(stride)
          padding_ = padding if isinstance(padding, str) else _pair(padding)
          dilation_ = _pair(dilation)
          super(Conv2d, self).__init__(
              in_channels, out_channels, kernel_size_, stride_, padding_, dilation_,
              False, _pair(0), groups, bias, padding_mode)
  
      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>`.
  
      * :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 à 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.
  
      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 (string, 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})`
          - Output: :math:`(N, 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'
      ):
          kernel_size_ = _triple(kernel_size)
          stride_ = _triple(stride)
          padding_ = padding if isinstance(padding, str) else _triple(padding)
          dilation_ = _triple(dilation)
          super(Conv3d, self).__init__(
              in_channels, out_channels, kernel_size_, stride_, padding_, dilation_,
              False, _triple(0), groups, bias, padding_mode)
  
      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):
          if padding_mode != 'zeros':
              raise ValueError('Only "zeros" padding mode is supported for {}'.format(self.__class__.__name__))
  
          super(_ConvTransposeNd, self).__init__(
              in_channels, out_channels, kernel_size, stride,
              padding, dilation, transposed, output_padding,
              groups, bias, padding_mode)
  
      # 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],
                          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:
              k = input.dim() - 2
              if len(output_size) == k + 2:
                  output_size = output_size[2:]
              if len(output_size) != k:
                  raise ValueError(
                      "output_size must have {} or {} elements (got {})"
                      .format(k, k + 2, len(output_size)))
  
              min_sizes = torch.jit.annotate(List[int], [])
              max_sizes = torch.jit.annotate(List[int], [])
              for d in range(k):
                  dim_size = ((input.size(d + 2) - 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((
                          "requested an output size of {}, but valid sizes range "
                          "from {} to {} (for an input of {})").format(
                              output_size, min_sizes, max_sizes, input.size()[2:]))
  
              res = torch.jit.annotate(List[int], [])
              for d in range(k):
                  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).
  
      This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
  
      * :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 à trous algorithm.
        It is harder to describe, but this `link`_ 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})`
          - Output: :math:`(N, 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}}`
  
      .. _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: _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'
      ):
          kernel_size = _single(kernel_size)
          stride = _single(stride)
          padding = _single(padding)
          dilation = _single(dilation)
          output_padding = _single(output_padding)
          super(ConvTranspose1d, self).__init__(
              in_channels, out_channels, kernel_size, stride, padding, dilation,
              True, output_padding, groups, bias, padding_mode)
  
      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, ...]`.
          output_padding = self._output_padding(
              input, output_size, self.stride, self.padding, self.kernel_size, self.dilation)  # type: ignore
          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).
  
      This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
  
      * :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 à 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:`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})`
          - Output: :math:`(N, 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])
  
      .. _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: _size_2_t = 0,
          output_padding: _size_2_t = 0,
          groups: int = 1,
          bias: bool = True,
          dilation: int = 1,
          padding_mode: str = 'zeros'
      ):
          kernel_size = _pair(kernel_size)
          stride = _pair(stride)
          padding = _pair(padding)
          dilation = _pair(dilation)
          output_padding = _pair(output_padding)
          super(ConvTranspose2d, self).__init__(
              in_channels, out_channels, kernel_size, stride, padding, dilation,
              True, output_padding, groups, bias, padding_mode)
  
      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, ...]`.
          output_padding = self._output_padding(
              input, output_size, self.stride, self.padding, self.kernel_size, self.dilation)  # type: ignore
  
          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).
  
      This module supports :ref:`TensorFloat32<tf32_on_ampere>`.
  
      * :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 à 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:`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})`
          - Output: :math:`(N, 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)
  
      .. _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: _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'
      ):
          kernel_size = _triple(kernel_size)
          stride = _triple(stride)
          padding = _triple(padding)
          dilation = _triple(dilation)
          output_padding = _triple(output_padding)
          super(ConvTranspose3d, self).__init__(
              in_channels, out_channels, kernel_size, stride, padding, dilation,
              True, output_padding, groups, bias, padding_mode)
  
      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, ...]`.
          output_padding = self._output_padding(
              input, output_size, self.stride, self.padding, self.kernel_size, self.dilation)  # type: ignore
  
          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):
      def __init__(self, *args, **kwargs):
          warnings.warn(
              "_ConvTransposeMixin is a deprecated internal class. "
              "Please consider using public APIs.")
          super(_ConvTransposeMixin, self).__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) -> None:  # type: ignore[override]
          # defined by parent class but using a protocol
          if self.has_uninitialized_params():  # type: ignore[misc]
              self.in_channels = input.shape[1]
              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()
  
  
  # 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 of the :class:`Conv1d` 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 (string, 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'
      ) -> None:
          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
          )
          self.weight = UninitializedParameter()
          self.out_channels = out_channels
          if bias:
              self.bias = UninitializedParameter()
  
  
  # 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 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 (string, 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
      ) -> None:
          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
          )
          self.weight = UninitializedParameter()
          self.out_channels = out_channels
          if bias:
              self.bias = UninitializedParameter()
  
  
  # 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 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 (string, 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'
      ) -> None:
          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
          )
          self.weight = UninitializedParameter()
          self.out_channels = out_channels
          if bias:
              self.bias = UninitializedParameter()
  
  
  # 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 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'
      ) -> None:
          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
          )
          self.weight = UninitializedParameter()
          self.out_channels = out_channels
          if bias:
              self.bias = UninitializedParameter()
  
  
  # 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 of the :class:`ConvTranspose2d` 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 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'
      ) -> None:
          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
          )
          self.weight = UninitializedParameter()
          self.out_channels = out_channels
          if bias:
              self.bias = UninitializedParameter()
  
  
  # 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 of the :class:`ConvTranspose3d` 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 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'
      ) -> None:
          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
          )
          self.weight = UninitializedParameter()
          self.out_channels = out_channels
          if bias:
              self.bias = UninitializedParameter()
  