
    &Vf                         d dl mZ d dlmZ d dlmZ  ed           G d de                      Z ed          d             Zd	S )
    )ops)keras_export)Mergezkeras.layers.Minimumc                       e Zd ZdZd ZdS )Minimuma9  Computes elementwise minimum 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.Minimum()([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.minimum([x1, x2])`
    >>> y = keras.layers.Minimum()([x1, x2])
    >>> out = keras.layers.Dense(4)(y)
    >>> model = keras.models.Model(inputs=[input1, input2], outputs=out)

    c                     |d         }t          dt          |                    D ]}t          j        |||                   }|S )Nr      )rangelenr   minimum)selfinputsoutputis       ]/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/layers/merging/minimum.py_merge_functionzMinimum._merge_function!   sF    q#f++&& 	4 	4A[33FF    N)__name__
__module____qualname____doc__r    r   r   r   r      s-         2    r   r   zkeras.layers.minimumc                 ,     t          di ||           S )ax  Functional interface to the `keras.layers.Minimum` layer.

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

    Returns:
        A tensor as the elementwise product of the inputs with the same
        shape as the inputs.

    Examples:

    >>> input_shape = (2, 3, 4)
    >>> x1 = np.random.rand(*input_shape)
    >>> x2 = np.random.rand(*input_shape)
    >>> y = keras.layers.minimum([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)
    >>> y = keras.layers.minimum([x1, x2])
    >>> out = keras.layers.Dense(4)(y)
    >>> model = keras.models.Model(inputs=[input1, input2], outputs=out)

    r   )r   )r   kwargss     r   r   r   (   s"    < 7VV$$$r   N)	keras.srcr   keras.src.api_exportr   #keras.src.layers.merging.base_merger   r   r   r   r   r   <module>r      s          - - - - - - 5 5 5 5 5 5 $%%    e   &%B $%%% % &%% % %r   