class_docstr: Performs elementwise subtraction.
  
  It takes as input a list of tensors of size 2 both of the
  same shape, and returns a single tensor (inputs[0] - inputs[1])
  of same shape.
  
  Examples:
  
  >>> input_shape = (2, 3, 4)
  >>> x1 = np.random.rand(*input_shape)
  >>> x2 = np.random.rand(*input_shape)
  >>> y = keras.layers.Subtract()([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 `subtracted = keras.layers.subtract([x1, x2])`
  >>> subtracted = keras.layers.Subtract()([x1, x2])
  >>> out = keras.layers.Dense(4)(subtracted)
  >>> model = keras.models.Model(inputs=[input1, input2], outputs=out)
code: def _merge_function(self, inputs):
      if len(inputs) != 2:
          raise ValueError(
              "A `Subtract` layer should be called on exactly 2 inputs. "
              f"Received: inputs={inputs}"
          )
      return ops.subtract(inputs[0], inputs[1])
  
file_path: keras/keras/layers/merging/subtract.py
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 2 for calling the Subtract obj's underlying member method code with example usage: obj._merge_function(inputs) and returns: ops