code: def _make_layer(self, block, channel, block_num, stride=1):
      downsample = None
      if stride != 1 or self.in_channel != channel * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2d(
                  self.in_channel,
                  channel * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(channel * block.expansion),
          )
  
      layers = []
      layers.append(
          block(
              self.in_channel,
              channel,
              downsample=downsample,
              stride=stride,
          )
      )
      self.in_channel = channel * block.expansion
  
      for _ in range(1, block_num):
          layers.append(block(self.in_channel, channel))
  
      return nn.Sequential(*layers)
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/ssd/src/res50_backbone.py
imports: import torch.nn
  import torch
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj._make_layer(block, channel, block_num, stride) and returns: nn