class_docstr: It calculates:
  first_log = K.log(K.clip(y, K.epsilon(), Double.MaxValue) + 1.)
  second_log = K.log(K.clip(x, K.epsilon(), Double.MaxValue) + 1.)
  and output K.mean(K.square(first_log - second_log))
  
  >>> loss = MeanSquaredLogarithmicError()
  creating: createZooKerasMeanSquaredLogarithmicError
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code: def __init__(self, bigdl_type="float"):
      super(MeanSquaredLogarithmicError, self).__init__(
          None, bigdl_type
      )
  
imports: import sys
  from bigdl.dllib.keras.base import ZooKerasCreator
  from bigdl.dllib.nn.criterion import Criterion
  from bigdl.dllib.utils.common import JTensor
label_desc: Use image node_id 1 to create a new MeanSquaredLogarithmicError object from inherited base classes: LossFunction with example: obj = MeanSquaredLogarithmicError(bigdl_type)