code: def identity_block(
      input_tensor,
      kernel_size,
      filters,
      stage,
      block,
      use_l2_regularizer=True,
  ):
      """The identity block is the block that has no conv layer at shortcut.
      Args:
        input_tensor: input tensor
        kernel_size: default 3, the kernel size of middle conv layer at main path
        filters: list of integers, the filters of 3 conv layer at main path
        stage: integer, current stage label, used for generating layer names
        block: 'a','b'..., current block label, used for generating layer names
        use_l2_regularizer: whether to use L2 regularizer on Conv layer.
      Returns:
        Output tensor for the block.
      """
      filters1, filters2, filters3 = filters
      if backend.image_data_format() == "channels_last":
          bn_axis = 3
      else:
          bn_axis = 1
      conv_name_base = "res" + str(stage) + block + "_branch"
      bn_name_base = "bn" + str(stage) + block + "_branch"
  
      x = layers.Conv2D(
          filters1,
          (1, 1),
          use_bias=False,
          kernel_initializer="he_normal",
          kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
          name=conv_name_base + "2a",
      )(input_tensor)
      x = layers.BatchNormalization(
          axis=bn_axis,
          momentum=BATCH_NORM_DECAY,
          epsilon=BATCH_NORM_EPSILON,
          name=bn_name_base + "2a",
      )(x)
      x = layers.Activation("relu")(x)
  
      x = layers.Conv2D(
          filters2,
          kernel_size,
          padding="same",
          use_bias=False,
          kernel_initializer="he_normal",
          kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
          name=conv_name_base + "2b",
      )(x)
      x = layers.BatchNormalization(
          axis=bn_axis,
          momentum=BATCH_NORM_DECAY,
          epsilon=BATCH_NORM_EPSILON,
          name=bn_name_base + "2b",
      )(x)
      x = layers.Activation("relu")(x)
  
      x = layers.Conv2D(
          filters3,
          (1, 1),
          use_bias=False,
          kernel_initializer="he_normal",
          kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
          name=conv_name_base + "2c",
      )(x)
      x = layers.BatchNormalization(
          axis=bn_axis,
          momentum=BATCH_NORM_DECAY,
          epsilon=BATCH_NORM_EPSILON,
          name=bn_name_base + "2c",
      )(x)
  
      x = layers.add([x, input_tensor])
      x = layers.Activation("relu")(x)
      return x
  
file_path: DALI/docs/examples/use_cases/tensorflow/resnet-n/resnet_model.py
imports: import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras import initializers
  from tensorflow.keras import models
  from tensorflow.keras import regularizers
  from nvutils import image_processing
label_desc: Use image node_id 2 for calling a global function with example usage: identity_block(input_tensor, kernel_size, filters, stage, block, use_l2_regularizer) and returns: x