docstr: save the forecaster.
  
  :param checkpoint_file: The checkpoint file location you want to load the forecaster.
docstr_tok: ["save","the","forecaster",".",":","param","checkpoint_file",":","The","checkpoint","file","location","you","want","to","load","the","forecaster","."]
code: def save(self, checkpoint_file):
      """
      save the forecaster.
  
      :param checkpoint_file: The checkpoint file location you want to load the forecaster.
      """
      if self.use_hpo:
          self.trainer.model = self.trainer.model.model
      self.trainer.save_checkpoint(checkpoint_file)
  
imports: import torch
  import numpy
  from pandas import Timedelta
  from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.forecaster.utils import read_csv, delete_folder
  from bigdl.chronos.metric.forecast_metrics import Evaluator
  from bigdl.chronos.model.autoformer import model_creator, loss_creator
  from torch.utils.data import TensorDataset, DataLoader
  from bigdl.chronos.model.autoformer.Autoformer import AutoFormer, _transform_config_to_namedtuple
  from bigdl.nano.utils.common import invalidInputError, invalidOperationError
  from bigdl.chronos.forecaster.utils import check_transformer_data
  from bigdl.chronos.pytorch import TSTrainer
  from bigdl.chronos.data import TSDataset
  from bigdl.nano.automl.hpo.space import Space
  from bigdl.chronos.forecaster.utils_hpo import GenericTSTransformerLightningModule, _config_has_search_space
  from bigdl.chronos.pytorch.context_manager import DummyForecasterContextManager, ForecasterContextManager
  from .utils_hpo import _format_metric_str
  import warnings
  from tempfile import TemporaryDirectory
  import os
label_desc: Use image node_id 12 for calling the AutoformerForecaster obj's underlying member method code with example usage: obj.save(checkpoint_file) without return types