
    0h                     h    d Z ddlmZ ddlmZ  ed       G d de             Z ed      d        Zy	)
zLayer that adds several inputs.    )_Merge)keras_exportzkeras.layers.Addc                       e Zd ZdZd Zy)Addas  Layer that adds 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 = tf.random.normal(input_shape)
    >>> x2 = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Add()([x1, x2])
    >>> print(y.shape)
    (2, 3, 4)

    Used 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)
    >>> # equivalent to `added = tf.keras.layers.add([x1, x2])`
    >>> added = tf.keras.layers.Add()([x1, x2])
    >>> out = tf.keras.layers.Dense(4)(added)
    >>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)

    c                 T    |d   }t        dt        |            D ]
  }|||   z  } |S )Nr      )rangelen)selfinputsoutputis       Y/var/www/html/engine/venv/lib/python3.12/site-packages/tf_keras/src/layers/merging/add.py_merge_functionzAdd._merge_function6   s8    q#f+& 	 AfQiF	     N)__name__
__module____qualname____doc__r    r   r   r   r      s    8r   r   zkeras.layers.addc                 $     t        di ||       S )a  Functional interface to the `tf.keras.layers.Add` layer.

    Args:
        inputs: A list of input tensors with the same shape.
        **kwargs: Standard layer keyword arguments.

    Returns:
        A tensor as the sum of the inputs. It has the same shape as the inputs.

    Examples:

    >>> input_shape = (2, 3, 4)
    >>> x1 = tf.random.normal(input_shape)
    >>> x2 = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.add([x1, x2])
    >>> print(y.shape)
    (2, 3, 4)

    Used 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)
    >>> added = tf.keras.layers.add([x1, x2])
    >>> out = tf.keras.layers.Dense(4)(added)
    >>> model = tf.keras.models.Model(inputs=[input1, input2], outputs=out)

    r   )r   )r   kwargss     r   addr   =   s    > 3==  r   N)r   &tf_keras.src.layers.merging.base_merger    tensorflow.python.util.tf_exportr   r   r   r   r   r   <module>r      sP    & : :  !!& ! "!H  !! "!r   