class_docstr: A loss that measures the mean squared value of the element-wise difference
  between the input and the target.
  
  # Arguments
  size_average: Boolean. Whether losses are averaged over observations for each
            mini-batch. Default is True. If False, the losses are instead
            summed for each mini-batch.
  
  >>> loss = MeanSquaredError()
  creating: createZooKerasMeanSquaredError
class_docstr_tok: ["A","loss","that","measures","the","mean","squared","value","of","the","element-wise","difference","between","the","input","and","the","target",".","#","Arguments","size_average",":","Boolean",".","Whether","losses","are","averaged","over","observations","for","each","mini-batch",".","Default","is","True",".","If","False",",","the","losses","are","instead","summed","for","each","mini-batch",".",">",">",">","loss","=","MeanSquaredError","(",")","creating",":","createZooKerasMeanSquaredError"]
code: def __init__(self, size_average=True, bigdl_type="float"):
      super(MeanSquaredError, self).__init__(
          None, bigdl_type, size_average
      )
  
imports: import sys
  from zoo.pipeline.api.keras.base import ZooKerasCreator
  from bigdl.nn.criterion import Criterion
  from bigdl.util.common import JTensor
label_desc: Use image node_id 1 to create a new MeanSquaredError object from inherited base classes: LossFunction with example: obj = MeanSquaredError(size_average, bigdl_type)