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 epochs: Number of epochs you want to train. The value defaults to 2.
  :param batch_size: Number of batch size you want to train. The value defaults to 32.
  :param validation_data: Data on which to evaluate the loss
         and any model metrics at the end of each epoch.
         The model will not be trained on this data.
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code: def fit(self, data, epochs=2, batch_size=32, validation_data=None):
      """
      :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 epochs: Number of epochs you want to train. The value defaults to 2.
      :param batch_size: Number of batch size you want to train. The value defaults to 32.
      :param validation_data: Data on which to evaluate the loss
             and any model metrics at the end of each epoch.
             The model will not be trained on this data.
      """
      self.model_config.update({"batch_size": batch_size})
      self.internal.fit_eval(
          data,
          validation_data=validation_data,
          epochs=epochs,
          verbose=1,
          **self.model_config,
      )
      self._fitted = True
  
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 2 for calling the MTNetForecaster obj's underlying member method code with example usage: obj.fit(data, epochs, batch_size, validation_data) without return types