class_docstr: Spatial 3D version of Dropout.
  This version performs the same function as Dropout, however it drops entire 3D feature maps
  instead of individual elements. If adjacent voxels within feature maps are strongly correlated
  (as is normally the case in early convolution layers) then regular dropout will not regularize
  the activations and will otherwise just result in an effective learning rate decrease.
  In this case, SpatialDropout3D will help promote independence between feature maps and
  should be used instead.
  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
  p: Fraction of the input units to drop. Float between 0 and 1.
  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.
  
  >>> spatialdropout3d = SpatialDropout3D(0.6, input_shape=(4, 12, 12, 16))
  creating: createZooKerasSpatialDropout3D
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code: def __init__(
      self, p=0.5, dim_ordering="th", input_shape=None, **kwargs
  ):
      super(SpatialDropout3D, self).__init__(
          None,
          float(p),
          dim_ordering,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from bigdl.util.common import INTMIN
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new SpatialDropout3D object from inherited base classes: ZooKerasLayer with example: obj = SpatialDropout3D(p, dim_ordering, input_shape)