class_docstr: Parent class for 3D pooling layers with adaptive kernel size.
  
  This class only exists for code reuse. It will never be an exposed API.
  
  Args:
    reduce_function: The reduction method to apply, e.g. `tf.reduce_max`.
    output_size: An integer or tuple/list of 3 integers specifying (pooled_dim1, pooled_dim2, pooled_dim3).
      The new size of output channels.
    data_format: A string,
      one of `channels_last` (default) 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)`.
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code: def __init__(
      self,
      reduce_function: Callable,
      output_size: Union[int, Iterable[int]],
      data_format=None,
      **kwargs,
  ):
      self.data_format = conv_utils.normalize_data_format(data_format)
      self.reduce_function = reduce_function
      self.output_size = conv_utils.normalize_tuple(
          output_size, 3, "output_size"
      )
      super().__init__(**kwargs)
  
imports: import tensorflow
  import tensorflow_addons.utils.keras_utils
  from typeguard import typechecked
  from typing import Union, Callable, Iterable
label_desc: Use image node_id 1 to create a new AdaptivePooling3D object from inherited base classes: tf.keras.layers with example: obj = AdaptivePooling3D(reduce_function, output_size, data_format)