class_docstr: Forecaster base model for lstm, mtnet, seq2seq and tcn forecasters.
class_docstr_tok: ["Forecaster","base","model","for","lstm",",","mtnet",",","seq2seq","and","tcn","forecasters","."]
docstr: restore the forecaster.
  
  :param checkpoint_file: The checkpoint file location you want to load the forecaster.
docstr_tok: ["restore","the","forecaster",".",":","param","checkpoint_file",":","The","checkpoint","file","location","you","want","to","load","the","forecaster","."]
code: def load(self, checkpoint_file):
      """
      restore the forecaster.
  
      :param checkpoint_file: The checkpoint file location you want to load the forecaster.
      """
      if self.distributed:
          self.internal.load(checkpoint_file)
      else:
          self.internal.restore(checkpoint_file)
  
file_path: analytics-zoo/pyzoo/zoo/chronos/forecaster/base_forecaster.py
imports: from zoo.chronos.forecaster.abstract import Forecaster
  from zoo.orca.data.shard import SparkXShards
  from zoo.chronos.forecaster.utils import np_to_creator, set_pytorch_seed, check_data, xshard_to_np, np_to_xshard
  import numpy
  from zoo.orca.learn.pytorch.estimator import Estimator
  from zoo.orca.learn.metrics import MSE, MAE
label_desc: Use image node_id 8 for calling the BasePytorchForecaster obj's underlying member method code with example usage: obj.load(checkpoint_file) without return types