class_docstr: Computes element-wise maximum on a list of inputs.
  
  It takes as input a list of tensors, all of the same shape,
  and returns a single tensor (also of the same shape).
  
  Examples:
  
  >>> input_shape = (2, 3, 4)
  >>> x1 = np.random.rand(*input_shape)
  >>> x2 = np.random.rand(*input_shape)
  >>> y = keras.layers.Maximum()([x1, x2])
  
  Usage in a Keras model:
  
  >>> input1 = keras.layers.Input(shape=(16,))
  >>> x1 = keras.layers.Dense(8, activation='relu')(input1)
  >>> input2 = keras.layers.Input(shape=(32,))
  >>> x2 = keras.layers.Dense(8, activation='relu')(input2)
  >>> # equivalent to `y = keras.layers.maximum([x1, x2])`
  >>> y = keras.layers.Maximum()([x1, x2])
  >>> out = keras.layers.Dense(4)(y)
  >>> model = keras.models.Model(inputs=[input1, input2], outputs=out)
code: def _merge_function(self, inputs):
      output = inputs[0]
      for i in range(1, len(inputs)):
          output = ops.maximum(output, inputs[i])
      return output
  
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.layers.merging.base_merge import Merge
label_desc: Use image node_id 1 for calling the Maximum obj's underlying member method code with example usage: obj._merge_function(inputs) and returns: output