class_docstr: Loss calculated as:y_true = K.clip(y_true, K.epsilon(), 1)
  y_pred = K.clip(y_pred, K.epsilon(), 1)
  and output K.sum(y_true * K.log(y_true / y_pred), axis=-1)
  
  >>> loss = KullbackLeiblerDivergence()
  creating: createZooKerasKullbackLeiblerDivergence
class_docstr_tok: ["Loss","calculated","as",":","y_true","=","K.clip","(","y_true",",","K.epsilon","(",")",",","1",")","y_pred","=","K.clip","(","y_pred",",","K.epsilon","(",")",",","1",")","and","output","K.sum","(","y_true","*","K.log","(","y_true","\/","y_pred",")",",","axis=-1",")",">",">",">","loss","=","KullbackLeiblerDivergence","(",")","creating",":","createZooKerasKullbackLeiblerDivergence"]
code: def __init__(self, bigdl_type="float"):
      super(KullbackLeiblerDivergence, 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 KullbackLeiblerDivergence object from inherited base classes: LossFunction with example: obj = KullbackLeiblerDivergence(bigdl_type)