code: def __init__(
      self, in_channel, out_channel, stride=1, downsample=None, **kwargs
  ):
      super(BasicBlock, self).__init__()
      self.conv1 = nn.Conv2d(
          in_channels=in_channel,
          out_channels=out_channel,
          kernel_size=3,
          stride=stride,
          padding=1,
          bias=False,
      )
      self.bn1 = nn.BatchNorm2d(out_channel)
      self.relu = nn.ReLU()
      self.conv2 = nn.Conv2d(
          in_channels=out_channel,
          out_channels=out_channel,
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
      self.bn2 = nn.BatchNorm2d(out_channel)
      self.downsample = downsample
  
file_path: deep-learning-for-image-processing/pytorch_classification/Test5_resnet/model.py
imports: import torch.nn
  import torch
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(in_channel, out_channel, stride, downsample)