def conv_block()

in example_zoo/tensorflow/models/keras_imagenet_main/official/resnet/keras/resnet_model.py [0:0]
50 lines of code
2 McCabe index (conditional complexity)

def conv_block(input_tensor,
               kernel_size,
               filters,
               stage,
               block,
               strides=(2, 2)):
  """A block that has a conv layer at shortcut.

  # Arguments
      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
      strides: Strides for the second conv layer in the block.

  # Returns
      Output tensor for the block.

  Note that from stage 3,
  the second conv layer at main path is with strides=(2, 2)
  And the shortcut should have strides=(2, 2) as well
  """
  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=regularizers.l2(L2_WEIGHT_DECAY),
                    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, strides=strides, padding='same',
                    use_bias=False, kernel_initializer='he_normal',
                    kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                    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=regularizers.l2(L2_WEIGHT_DECAY),
                    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)

  shortcut = layers.Conv2D(filters3, (1, 1), strides=strides, use_bias=False,
                           kernel_initializer='he_normal',
                           kernel_regularizer=regularizers.l2(L2_WEIGHT_DECAY),
                           name=conv_name_base + '1')(input_tensor)
  shortcut = layers.BatchNormalization(axis=bn_axis,
                                       momentum=BATCH_NORM_DECAY,
                                       epsilon=BATCH_NORM_EPSILON,
                                       name=bn_name_base + '1')(shortcut)

  x = layers.add([x, shortcut])
  x = layers.Activation('relu')(x)
  return x