class_docstr: UpSampling layer for 2D inputs.
  Repeats the 1st, 2nd and 3rd dimensions of the data by
  size[0], size[1] and size[2] respectively.
  Data format currently supported for this layer is dim_ordering='th' (Channel First).
  The input of this layer should be 5D.
  
  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
  size: Int tuple of length 3. UpSampling factors for dim1, dim2 and dim3. Default is (2, 2, 2).
  dim_ordering: Format of input data. Only 'th' (Channel First) is supported for now.
  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.
  
  >>> upsampling3d = UpSampling3D(size=(1, 2, 3), input_shape=(3, 16, 16, 16))
  creating: createZooKerasUpSampling3D
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code: def __init__(
      self,
      size=(2, 2, 2),
      dim_ordering="th",
      input_shape=None,
      **kwargs,
  ):
      super(UpSampling3D, self).__init__(
          None,
          size,
          dim_ordering,
          list(input_shape) if input_shape else None,
          **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 UpSampling3D object from inherited base classes: ZooKerasLayer with example: obj = UpSampling3D(size, dim_ordering, input_shape)