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: Set summary information during the training process for visualization purposes.
  Saved summary can be viewed via TensorBoard.
  In order to take effect, it needs to be called before fit.
  
  Training summary will be saved to 'log_dir/app_name/train'
  and validation summary (if any) will be saved to 'log_dir/app_name/validation'.
  
  # Arguments
  :param log_dir: The base directory path to store training and validation logs.
  :param app_name: The name of the application.
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code: def set_tensorboard(self, log_dir, app_name):
      """
      Set summary information during the training process for visualization purposes.
      Saved summary can be viewed via TensorBoard.
      In order to take effect, it needs to be called before fit.
  
      Training summary will be saved to 'log_dir/app_name/train'
      and validation summary (if any) will be saved to 'log_dir/app_name/validation'.
  
      # Arguments
      :param log_dir: The base directory path to store training and validation logs.
      :param app_name: The name of the application.
      """
      callZooFunc(
          self.bigdl_type,
          "estimatorSetTensorBoard",
          self.value,
          log_dir,
          app_name,
      )
  
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 5 for calling the Estimator obj's underlying member method code with example usage: obj.set_tensorboard(log_dir, app_name) without return types