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)
class_docstr_tok: ["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",")"]
docstr: :param data: The data support following formats:
  
         | 1. data's shape is (num_samples, (long_series_num+1)*series_length, feature_dim)
  
  :param batch_size: predict batch size. The value will not affect evaluate
         result but will affect resources cost(e.g. memory and time).
  
  :return: A numpy.ndarray with shape of (num_samples, feature_dum).
docstr_tok: [":","param","data",":","The","data","support","following","formats",":","|","1.","data","'s","shape","is","(","num_samples",",","(","long_series_num+1",")","*","series_length",",","feature_dim",")",":","param","batch_size",":","predict","batch","size",".","The","value","will","not","affect","evaluate","result","but","will","affect","resources","cost","(","e.g",".","memory","and","time",")",".",":","return",":","A","numpy.ndarray","with","shape","of","(","num_samples",",","feature_dum",")","."]
code: def predict(self, data, batch_size=32):
      """
      :param data: The data support following formats:
  
             | 1. data's shape is (num_samples, (long_series_num+1)*series_length, feature_dim)
  
      :param batch_size: predict batch size. The value will not affect evaluate
             result but will affect resources cost(e.g. memory and time).
  
      :return: A numpy.ndarray with shape of (num_samples, feature_dum).
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if not self._fitted:
          invalidInputError(
              False,
              "You must call fit or restore first before calling predict!",
          )
      yhat = self.internal.predict(data, batch_size=batch_size)
      return yhat
  
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 3 for calling the MTNetForecaster obj's underlying member method code with example usage: obj.predict(data, batch_size) and returns: yhat