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                     @   sD   d dl mZ d dlmZ d dlmZ eddgG dd deZdS )	    )ops)keras_export)BaseGlobalPoolingz#keras.layers.GlobalAveragePooling3Dzkeras.layers.GlobalAvgPool3Dc                       s*   e Zd ZdZd fdd	Zdd Z  ZS )	GlobalAveragePooling3Da#  Global average pooling operation for 3D data.

    Args:
        data_format: string, either `"channels_last"` or `"channels_first"`.
            The ordering of the dimensions in the inputs. `"channels_last"`
            corresponds to inputs with shape
            `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
            while `"channels_first"` corresponds to inputs with shape
            `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
            It defaults to the `image_data_format` value found in your Keras
            config file at `~/.keras/keras.json`. If you never set it, then it
            will be `"channels_last"`.
        keepdims: A boolean, whether to keep the temporal dimension or not.
            If `keepdims` is `False` (default), the rank of the tensor is
            reduced for spatial dimensions. If `keepdims` is `True`, the
            spatial dimension are retained with length 1.
            The behavior is the same as for `tf.reduce_mean` or `np.mean`.

    Input shape:

    - If `data_format='channels_last'`:
        5D tensor with shape:
        `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
    - If `data_format='channels_first'`:
        5D tensor with shape:
        `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`

    Output shape:

    - If `keepdims=False`:
        2D tensor with shape `(batch_size, channels)`.
    - If `keepdims=True`:
        - If `data_format="channels_last"`:
            5D tensor with shape `(batch_size, 1, 1, 1, channels)`
        - If `data_format="channels_first"`:
            5D tensor with shape `(batch_size, channels, 1, 1, 1)`

    Example:

    >>> x = np.random.rand(2, 4, 5, 4, 3)
    >>> y = keras.layers.GlobalAveragePooling3D()(x)
    >>> y.shape
    (2, 3)
    NFc                    s   t  jdd||d| d S )N   )pool_dimensionsdata_formatkeepdims )super__init__)selfr   r	   kwargs	__class__r
   e/home/ubuntu/.local/lib/python3.10/site-packages/keras/src/layers/pooling/global_average_pooling3d.pyr   :   s   
zGlobalAveragePooling3D.__init__c                 C   s6   | j dkrtj|g d| jdS tj|g d| jdS )Nchannels_last)      r   )axisr	   )r   r      )r   r   meanr	   )r   inputsr
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zGlobalAveragePooling3D.call)NF)__name__
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