class_docstr: Example:
      >>> #The dataset is split into x_train, x_val, x_test, y_train, y_val, y_test
      >>> model = MTNetForecaster(target_dim=1,
                                  feature_dim=x_train.shape[-1],
                                  long_series_num=6,
                                  series_length=2
                                  )
      >>> model.fit(data=(x_train, y_train),
                    validation_data=(x_val, y_val),
                    epochs=2,
                    batch_size=32)
      >>> model.predict(x_test, batch_size=batch_size)
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docstr: :param data: The data support following formats:
  
      | 1. a numpy ndarray tuple (x, y):
      | x's shape is (num_samples, (long_series_num+1)*series_length, feature_dim)
      | y's shape is (num_samples, target_dim)
  
  :param metric:  metric is the evaluation metric name to optimize, e.g. ["mae"].
  :param batch_size: evaluate batch size. The value will not affect evaluate
         result but will affect resources cost(e.g. memory and time).
  :param multioutput: Defines aggregating of multiple output values.
         String in ['raw_values', 'uniform_average']. The value defaults to
         'raw_values'.
  
  :return: A list of evaluation results. Calculation results for each metrics.
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code: def evaluate(
      self,
      data,
      metric=["mae"],
      batch_size=32,
      multioutput="raw_values",
  ):
      """
      :param data: The data support following formats:
  
          | 1. a numpy ndarray tuple (x, y):
          | x's shape is (num_samples, (long_series_num+1)*series_length, feature_dim)
          | y's shape is (num_samples, target_dim)
  
      :param metric:  metric is the evaluation metric name to optimize, e.g. ["mae"].
      :param batch_size: evaluate batch size. The value will not affect evaluate
             result but will affect resources cost(e.g. memory and time).
      :param multioutput: Defines aggregating of multiple output values.
             String in ['raw_values', 'uniform_average']. The value defaults to
             'raw_values'.
  
      :return: A list of evaluation results. Calculation results for each metrics.
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if not self._fitted:
          invalidInputError(
              False,
              "You must call fit or restore first before calling evaluate!",
          )
      yhat = self.internal.predict(data[0], batch_size=batch_size)
      return [
          Evaluator.evaluate(
              m, y_true=data[1], y_pred=yhat, aggregate=multioutput
          )
          for m in metric
      ]
  
file_path: BigDL/python/chronos/src/bigdl/chronos/forecaster/tf/mtnet_forecaster.py
imports: from bigdl.chronos.model.tf2.MTNet_keras import MTNetKeras
  from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.metric.forecast_metrics import Evaluator
label_desc: Use image node_id 4 for calling the MTNetForecaster obj's underlying member method code with example usage: obj.evaluate(data, metric, batch_size, multioutput) and returns: unknown