docstr: Optimize the model by the given options
  
  :param x: ndarray or list of ndarray for local mode.
            RDD[Sample] for distributed mode
  :param y: ndarray or list of ndarray for local mode and would be None for cluster mode.
      is_distributed: used to control run in local or cluster. the default value is False.
      NB: if is_distributed=true, x should be RDD[Sample] and y should be None
  :param is_distributed: Whether to train in local mode or distributed mode
  :return:
      A Numpy array or RDD[Sample] of predictions.
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code: def fit(
      self,
      x,
      y=None,
      batch_size=32,
      nb_epoch=10,
      verbose=1,
      callbacks=None,
      validation_split=0.0,
      validation_data=None,
      shuffle=True,
      class_weight=None,
      sample_weight=None,
      initial_epoch=0,
      is_distributed=False,
  ):
      """Optimize the model by the given options
  
      :param x: ndarray or list of ndarray for local mode.
                RDD[Sample] for distributed mode
      :param y: ndarray or list of ndarray for local mode and would be None for cluster mode.
          is_distributed: used to control run in local or cluster. the default value is False.
          NB: if is_distributed=true, x should be RDD[Sample] and y should be None
      :param is_distributed: Whether to train in local mode or distributed mode
      :return:
          A Numpy array or RDD[Sample] of predictions.
      """
      if callbacks:
          invalidInputError(
              False, "We don't support callbacks in fit for now"
          )
      if class_weight:
          unsupport_exp("class_weight")
      if sample_weight:
          unsupport_exp("sample_weight")
      if initial_epoch != 0:
          unsupport_exp("initial_epoch")
      if shuffle is not True:
          unsupport_exp("shuffle")
      if validation_split != 0.0:
          unsupport_exp("validation_split")
      bopt = self.__create_optimizer(
          x=x,
          y=y,
          batch_size=batch_size,
          nb_epoch=nb_epoch,
          validation_data=validation_data,
          is_distributed=is_distributed,
      )
      bopt.optimize()
  
file_path: BigDL/python/dllib/src/bigdl/dllib/nn/keras/backend.py
imports: from bigdl.dllib.nn.keras.optimization import *
  from bigdl.dllib.utils.common import *
label_desc: Use image node_id 4 for calling the KerasModelWrapper obj's underlying member method code with example usage: obj.fit(x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, is_distributed) without return types