code: def identity_block(input, kernel_size, n_filters, stage, block):
      """The identity block where there is no conv layer at shortcut.
  
      Parameters
      ----------
      input : tf tensor
          Input tensor from above layer.
      kernel_size : int
          The kernel size of middle conv layer at main path.
      n_filters : list of integers
          The numbers of filters for 3 conv layer at main path.
      stage : int
          Current stage label.
      block : str
          Current block label.
  
      Returns
      -------
          Output tensor of this block.
  
      """
      filters1, filters2, filters3 = n_filters
      conv_name_base = "res" + str(stage) + block + "_branch"
      bn_name_base = "bn" + str(stage) + block + "_branch"
  
      x = Conv2d(
          filters1,
          (1, 1),
          W_init=tf.initializers.he_normal(),
          name=conv_name_base + "2a",
      )(input)
      x = BatchNorm(name=bn_name_base + "2a", act="relu")(x)
  
      ks = (kernel_size, kernel_size)
      x = Conv2d(
          filters2,
          ks,
          padding="SAME",
          W_init=tf.initializers.he_normal(),
          name=conv_name_base + "2b",
      )(x)
      x = BatchNorm(name=bn_name_base + "2b", act="relu")(x)
  
      x = Conv2d(
          filters3,
          (1, 1),
          W_init=tf.initializers.he_normal(),
          name=conv_name_base + "2c",
      )(x)
      x = BatchNorm(name=bn_name_base + "2c")(x)
  
      x = Elementwise(tf.add, act="relu")([x, input])
      return x
  
file_path: TensorLayer/tensorlayer/models/resnet.py
imports: import os
  import tensorflow
  from tensorlayer import logging
  from tensorlayer.files import assign_weights, load_npz, maybe_download_and_extract
  from tensorlayer.layers import BatchNorm, Conv2d, Dense, Elementwise, GlobalMeanPool2d, Input, MaxPool2d
  from tensorlayer.models import Model
label_desc: Use image node_id 1 for calling a global function with example usage: identity_block(input, kernel_size, n_filters, stage, block) and returns: x