class_docstr: Conv2d layer with ResNet initialization:
  
  Reference: "Deep Residual Learning for Image Recognition" by He et al.
  https://arxiv.org/abs/1512.03385
class_docstr_tok: ["Conv2d","layer","with","ResNet","initialization",":","Reference",":","``","Deep","Residual","Learning","for","Image","Recognition","''","by","He","et","al",".","https",":","\/\/arxiv.org\/abs\/1512.03385"]
code: 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",
      device=None,
      dtype=None,
      weight_init: str = "default",
      bias_init: str = "default",
  ):
      super(Conv2d, self).__init__(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=groups,
          bias=bias,
          padding_mode=padding_mode,
          device=device,
          dtype=dtype,
      )
  
      # Weight Init
      assert weight_init in ["default", "he_normal"]
      if weight_init == "he_normal":
          init.kaiming_normal_(self.weight)
  
      # Bias Init
      assert bias_init in ["default", "zeros"]
      if self.bias is not None:
          if bias_init == "zeros":
              init.zeros_(self.bias)
  
imports: from typing import Union
  from torch import nn
  from torch.nn import init
  from torch.nn.common_types import _size_2_t
label_desc: Use image node_id 1 to create a new Conv2d object from inherited base classes: nn with example: obj = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode, device, dtype, weight_init, bias_init)