class_docstr: TSPipeline is an E2E solution for time series analysis (only forecasting task for now).
  You can use TSPipeline to:
  
  1. Further development on the prototype. (predict, evaluate, incremental fit)
  
  2. Deploy the model to their scenario. (save, load)
docstr: Incremental fitting
  
  :param data: data can be a TSDataset or data creator(will be supported).
         the TSDataset should follow the same operations as the training
         TSDataset used in AutoTSEstimator.fit.
  :param validation_data: validation data, same format as data.
  :param epochs: incremental fitting epoch. The value defaults to 1.
  :param metric: evaluate metric.
docstr_tok: ["Incremental","fitting",":","param","data",":","data","can","be","a","TSDataset","or","data","creator","(","will","be","supported",")",".","the","TSDataset","should","follow","the","same","operations","as","the","training","TSDataset","used","in","AutoTSEstimator.fit",".",":","param","validation_data",":","validation","data",",","same","format","as","data",".",":","param","epochs",":","incremental","fitting","epoch",".","The","value","defaults","to","1.",":","param","metric",":","evaluate","metric","."]
code: def fit(self, data, validation_data=None, epochs=1, metric="mse"):
      """
      Incremental fitting
  
      :param data: data can be a TSDataset or data creator(will be supported).
             the TSDataset should follow the same operations as the training
             TSDataset used in AutoTSEstimator.fit.
      :param validation_data: validation data, same format as data.
      :param epochs: incremental fitting epoch. The value defaults to 1.
      :param metric: evaluate metric.
      """
      x, y = self._tsdataset_to_numpy(data, is_predict=False)
      if validation_data is None:
          x_val, y_val = x, y
      else:
          x_val, y_val = self._tsdataset_to_numpy(
              validation_data, is_predict=False
          )
  
      res = self._best_model.fit_eval(
          data=(x, y), validation_data=(x_val, y_val), metric=metric
      )
      return res
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/tspipeline.py
imports: import os
  from zoo.chronos.data import TSDataset
  from zoo.orca.automl.metrics import Evaluator
label_desc: Use image node_id 6 for calling the TSPipeline obj's underlying member method code with example usage: obj.fit(data, validation_data, epochs, metric) and returns: res