parameters: (input_tensor,
                     kernel_size,
                     filters,
                     stage,
                     block,
                     use_l2_regularizer=True)
return_statement: return x
docstring: 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.
docstring_summary: 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.
docstring_tokens: 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 .
function: 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
function_tokens: def identity_block ( input_tensor , kernel_size , filters , stage , block , use_l2_regularizer = True ) : 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