class_docstr: A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and
  more features.
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docstr: Extra keyword arguments supported in addition to those in
  `torch.nn.Conv2d`:
  
  Args:
      norm (nn.Module, optional): a normalization layer
      activation (callable(Tensor) -> Tensor): a callable
          activation function
  
  It assumes that norm layer is used before activation.
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code: def __init__(self, *args, **kwargs):
      """Extra keyword arguments supported in addition to those in
      `torch.nn.Conv2d`:
  
      Args:
          norm (nn.Module, optional): a normalization layer
          activation (callable(Tensor) -> Tensor): a callable
              activation function
  
      It assumes that norm layer is used before activation.
      """
      norm = kwargs.pop("norm", None)
      activation = kwargs.pop("activation", None)
      super().__init__(*args, **kwargs)
  
      self.norm = norm
      self.activation = activation
  
imports: import copy
  import math
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
label_desc: Use image node_id 1 to create a new Conv2d object from inherited base classes: torch.nn with example: obj = Conv2d()