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: Evaluate the time series pipeline.
  
  :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 metrics: list. The evaluation metric name to optimize. e.g. ["mse"]
  :param multioutput: Defines aggregating of multiple output values.
         String in ['raw_values', 'uniform_average']. The value defaults to
         'uniform_average'.
  :param batch_size: predict batch_size, the process will cost more time
         if batch_size is small while cost less memory. The param is only
         effective when data is a TSDataset. The values defaults to 32.
docstr_tok: ["Evaluate","the","time","series","pipeline",".",":","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","metrics",":","list",".","The","evaluation","metric","name","to","optimize",".","e.g",".","[","``","mse","''","]",":","param","multioutput",":","Defines","aggregating","of","multiple","output","values",".","String","in","[","'raw_values","'",",","'uniform_average","'","]",".","The","value","defaults","to","'uniform_average","'",".",":","param","batch_size",":","predict","batch_size",",","the","process","will","cost","more","time","if","batch_size","is","small","while","cost","less","memory",".","The","param","is","only","effective","when","data","is","a","TSDataset",".","The","values","defaults","to","32","."]
code: def evaluate(
      self,
      data,
      metrics=["mse"],
      multioutput="uniform_average",
      batch_size=32,
  ):
      """
      Evaluate the time series pipeline.
  
      :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 metrics: list. The evaluation metric name to optimize. e.g. ["mse"]
      :param multioutput: Defines aggregating of multiple output values.
             String in ['raw_values', 'uniform_average']. The value defaults to
             'uniform_average'.
      :param batch_size: predict batch_size, the process will cost more time
             if batch_size is small while cost less memory. The param is only
             effective when data is a TSDataset. The values defaults to 32.
      """
      # predict
      x, y = self._tsdataset_to_numpy(data, is_predict=False)
      yhat = self._best_model.predict(x, batch_size=batch_size)
      yhat = self._tsdataset_unscale(yhat)
      # unscale
      y = self._tsdataset_unscale(y)
      # evaluate
      eval_result = [
          Evaluator.evaluate(
              m, y_true=y, y_pred=yhat, multioutput=multioutput
          )
          for m in metrics
      ]
      return eval_result
  
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 2 for calling the TSPipeline obj's underlying member method code with example usage: obj.evaluate(data, metrics, multioutput, batch_size) and returns: eval_result