class_docstr: Applies max pooling operation for spatial data.
  The input of this layer should be 4D.
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  pool_size: Int tuple of length 2 corresponding to the downscale vertically and horizontally.
             Default is (2, 2), which will halve the image in each dimension.
  strides: Int tuple of length 2. Stride values.
           Default is None, and in this case it will be equal to pool_size.
  border_mode: Either 'valid' or 'same'. Default is 'valid'.
  dim_ordering: Format of input data. Either 'th' (Channel First) or 'tf' (Channel Last).
                Default is 'th'.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> maxpooling2d = MaxPooling2D((2, 2), input_shape=(3, 32, 32), name="maxpooling2d_1")
  creating: createZooKerasMaxPooling2D
code: def __init__(
      self,
      pool_size=(2, 2),
      strides=None,
      border_mode="valid",
      dim_ordering="th",
      input_shape=None,
      pads=None,
      **kwargs,
  ):
      super(MaxPooling2D, self).__init__(
          None,
          pool_size,
          strides,
          border_mode,
          dim_ordering,
          list(input_shape) if input_shape else None,
          pads,
          **kwargs,
      )
  
imports: import sys
  from ..engine.topology import ZooKerasLayer
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new MaxPooling2D object from inherited base classes: ZooKerasLayer with example: obj = MaxPooling2D(pool_size, strides, border_mode, dim_ordering, input_shape, pads)