class_docstr: Max pooling layer for 3D inputs (e.g. volumes).
  
  Args:
    pool_size: An integer or tuple/list of 3 integers:
      (pool_depth, pool_height, pool_width)
      specifying the size of the pooling window.
      Can be a single integer to specify the same value for
      all spatial dimensions.
    strides: An integer or tuple/list of 3 integers,
      specifying the strides of the pooling operation.
      Can be a single integer to specify the same value for
      all spatial dimensions.
    padding: A string. The padding method, either 'valid' or 'same'.
      Case-insensitive.
    data_format: A string. The ordering of the dimensions in the inputs.
      `channels_last` (default) and `channels_first` are supported.
      `channels_last` corresponds to inputs with shape
      `(batch, depth, height, width, channels)` while `channels_first`
      corresponds to inputs with shape
      `(batch, channels, depth, height, width)`.
    name: A string, the name of the layer.
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code: def __init__(
      self,
      pool_size,
      strides,
      padding="valid",
      data_format="channels_last",
      name=None,
      **kwargs,
  ):
      if strides is None:
          raise ValueError("Argument `strides` must not be None.")
      super(MaxPooling3D, self).__init__(
          pool_size=pool_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          name=name,
          **kwargs,
      )
  
imports: import warnings
  from tensorflow.python.keras import layers
  from tensorflow.python.keras.legacy_tf_layers import base
label_desc: Use image node_id 1 to create a new MaxPooling3D object from inherited base classes: keras_layers, base with example: obj = MaxPooling3D(pool_size, strides, padding, data_format, name)