class_docstr: Average Pooling with adaptive kernel size.
  
  Args:
    output_size: An integer or tuple/list of a single integer, specifying pooled_features.
      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, steps, channels)` while `channels_first`
      corresponds to inputs with shape `(batch, channels, steps)`.
  
  Input shape:
    - If `data_format='channels_last'`:
      3D tensor with shape `(batch, steps, channels)`.
    - If `data_format='channels_first'`:
      3D tensor with shape `(batch, channels, steps)`.
  
  Output shape:
    - If `data_format='channels_last'`:
      3D tensor with shape `(batch_size, pooled_steps, channels)`.
    - If `data_format='channels_first'`:
      3D tensor with shape `(batch_size, channels, pooled_steps)`.
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code: def __init__(
      self,
      output_size: Union[int, Iterable[int]],
      data_format=None,
      **kwargs,
  ):
      super().__init__(
          tf.reduce_mean, output_size, data_format, **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 AdaptiveAveragePooling1D object from inherited base classes: AdaptivePooling1D with example: obj = AdaptiveAveragePooling1D(output_size, data_format)