code: def make_layer(self, block, out_channels, blocks, stride=1):
      downsample = None
      if (stride != 1) or (self.in_channels != out_channels):
          downsample = nn.Sequential(
              conv3x3(self.in_channels, out_channels, stride=stride),
              nn.BatchNorm2d(out_channels),
          )
      layers = []
      layers.append(
          block(self.in_channels, out_channels, stride, downsample)
      )
      self.in_channels = out_channels
      for i in range(1, blocks):
          layers.append(block(out_channels, out_channels))
      return nn.Sequential(*layers)
  
file_path: pytorch-tutorial/tutorials/02-intermediate/deep_residual_network/main.py
imports: import torch
  import torch.nn
  import torchvision
  import torchvision.transforms
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj.make_layer(block, out_channels, blocks, stride) and returns: nn