docstr: Load the forecaster.
  
  :params checkpoint_file: The checkpoint file location you want to load the forecaster.
docstr_tok: ["Load","the","forecaster",".",":","params","checkpoint_file",":","The","checkpoint","file","location","you","want","to","load","the","forecaster","."]
code: def load(self, checkpoint_file):
      """
      Load the forecaster.
  
      :params checkpoint_file: The checkpoint file location you want to load the forecaster.
      """
      if self.distributed:
          self.internal.load(checkpoint_file)
      else:
          self.internal = keras.models.load_model(
              checkpoint_file, custom_objects=self.custom_objects_config
          )
      self.fitted = True
  
imports: import warnings
  import keras
  import tensorflow
  import numpy
  from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.data import TSDataset
  from bigdl.chronos.metric.forecast_metrics import Evaluator
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.chronos.forecaster.tf.utils import np_to_data_creator, tsdata_to_data_creator, np_to_xshards, xshard_to_np, np_to_tfdataset
label_desc: Use image node_id 9 for calling the BaseTF2Forecaster obj's underlying member method code with example usage: obj.load(checkpoint_file) without return types