code: def _make_layer(
      block, in_channel, channel, block_num, name, strides=1
  ):
      downsample = None
      if strides != 1 or in_channel != channel * block.expansion:
          downsample = Sequential(
              [
                  layers.Conv2D(
                      channel * block.expansion,
                      kernel_size=1,
                      strides=strides,
                      use_bias=False,
                      name="conv1",
                  ),
                  layers.BatchNormalization(
                      momentum=0.9, epsilon=1.001e-5, name="BatchNorm"
                  ),
              ],
              name="shortcut",
          )
  
      layers_list = []
      layers_list.append(
          block(
              channel,
              downsample=downsample,
              strides=strides,
              name="unit_1",
          )
      )
  
      for index in range(1, block_num):
          layers_list.append(
              block(channel, name="unit_" + str(index + 1))
          )
  
      return Sequential(layers_list, name=name)
  
file_path: deep-learning-for-image-processing/tensorflow_classification/Test5_resnet/model.py
imports: from tensorflow.keras import layers, Model, Sequential
label_desc: Use image node_id 1 for calling a global function with example usage: _make_layer(block, in_channel, channel, block_num, name, strides) and returns: Sequential