class_docstr: Estimator class for training and evaluation BigDL models.
  
  Estimator wraps a model, and provide an uniform training, evaluation or prediction operation on
  both local host and distributed spark environment.
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docstr: Train model with provided trainSet and criterion.
  The training will end until the endTrigger is triggered.
  During the training, if checkPointTrigger is defined and triggered,
  the model will be saved to modelDir. And if validationSet and validationMethod
  is defined, the model will be evaluated at the checkpoint.
  :param train_set: training FeatureSet, a FeatureSet[Sample[T]]
  :param criterion: Loss function
  :param end_trigger: When to finish the training
  :param checkpoint_trigger: When to save a checkpoint and evaluate model.
  :param validation_set: Validation FeatureSet, a FeatureSet[Sample[T]]
  :param validation_method: Validation Methods.
  :param batch_size:
  :return: Estimator
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code: def train(
      self,
      train_set,
      criterion,
      end_trigger=None,
      checkpoint_trigger=None,
      validation_set=None,
      validation_method=None,
      batch_size=32,
  ):
      """
      Train model with provided trainSet and criterion.
      The training will end until the endTrigger is triggered.
      During the training, if checkPointTrigger is defined and triggered,
      the model will be saved to modelDir. And if validationSet and validationMethod
      is defined, the model will be evaluated at the checkpoint.
      :param train_set: training FeatureSet, a FeatureSet[Sample[T]]
      :param criterion: Loss function
      :param end_trigger: When to finish the training
      :param checkpoint_trigger: When to save a checkpoint and evaluate model.
      :param validation_set: Validation FeatureSet, a FeatureSet[Sample[T]]
      :param validation_method: Validation Methods.
      :param batch_size:
      :return: Estimator
      """
      callZooFunc(
          self.bigdl_type,
          "estimatorTrain",
          self.value,
          train_set,
          criterion,
          end_trigger,
          checkpoint_trigger,
          validation_set,
          validation_method,
          batch_size,
      )
      return self
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/estimator/estimator.py
imports: from bigdl.util.common import JavaValue
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 8 for calling the Estimator obj's underlying member method code with example usage: obj.train(train_set, criterion, end_trigger, checkpoint_trigger, validation_set, validation_method, batch_size) and returns: self