code: def multiply(inputs, **kwargs):
      """Functional interface to the `Multiply` layer.
  
      Example:
  
      >>> x1 = np.arange(3.0)
      >>> x2 = np.arange(3.0)
      >>> tf.keras.layers.multiply([x1, x2])
      <tf.Tensor: shape=(3,), dtype=float32, numpy=array([0., 1., 4.], ...)>
  
      Usage in a functional model:
  
      >>> input1 = tf.keras.layers.Input(shape=(16,))
      >>> x1 = tf.keras.layers.Dense(8, activation='relu')(input1) #shape=(None, 8)
      >>> input2 = tf.keras.layers.Input(shape=(32,))
      >>> x2 = tf.keras.layers.Dense(8, activation='relu')(input2) #shape=(None, 8)
      >>> out = tf.keras.layers.multiply([x1,x2]) #shape=(None, 8)
      >>> out = tf.keras.layers.Dense(4)(out)
      >>> 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 element-wise product of the inputs.
      """
      return Multiply(**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 3 for calling a global function with example usage: multiply(inputs) and returns: Multiply