code: def average(inputs, **kwargs):
      """Functional interface to the `tf.keras.layers.Average` layer.
  
      Example:
  
      >>> x1 = np.ones((2, 2))
      >>> x2 = np.zeros((2, 2))
      >>> y = tf.keras.layers.Average()([x1, x2])
      >>> y.numpy().tolist()
      [[0.5, 0.5], [0.5, 0.5]]
  
      Usage in a functional model:
  
      >>> input1 = tf.keras.layers.Input(shape=(16,))
      >>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1)
      >>> input2 = tf.keras.layers.Input(shape=(32,))
      >>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2)
      >>> avg = tf.keras.layers.Average()([x1, x2])
      >>> out = tf.keras.layers.Dense(4)(avg)
      >>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)
  
      Args:
          inputs: A list of input tensors (at least 2).
          **kwargs: Standard layer keyword arguments.
  
      Returns:
          A tensor, the average of the inputs.
  
      Raises:
        ValueError: If there is a shape mismatch between the inputs and the shapes
          cannot be broadcasted to match.
      """
      return Average(**kwargs)(inputs)
  
imports: from tensorflow.python.keras import backend
  from tensorflow.python.keras.engine import base_layer_utils
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
label_desc: Use image node_id 4 for calling a global function with example usage: average(inputs) and returns: Average