code: def __init__(
      self,
      in_ch: int,
      out_ch: int,
      kernel_size: int = 3,
      dilation: int = 1,
  ):
      super().__init__()
  
      padding = kernel_size // 2 if dilation == 1 else dilation
      self.conv = nn.Conv2d(
          in_ch,
          out_ch,
          kernel_size,
          padding=padding,
          dilation=dilation,
          bias=False,
      )
      self.bn = nn.BatchNorm2d(out_ch)
      self.relu = nn.ReLU(inplace=True)
  
imports: from typing import Union, List
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ConvBNReLU object from inherited base classes: nn with example: obj = ConvBNReLU(in_ch, out_ch, kernel_size, dilation)