docstr: Obtain context manager from forecaster.
  :param thread_num: int, the num of thread limit. The value is set to None by
         default where no limit is set.
  :return: a context manager.
docstr_tok: ["Obtain","context","manager","from","forecaster",".",":","param","thread_num",":","int",",","the","num","of","thread","limit",".","The","value","is","set","to","None","by","default","where","no","limit","is","set",".",":","return",":","a","context","manager","."]
code: def get_context(self, thread_num=None):
      """
      Obtain context manager from forecaster.
      :param thread_num: int, the num of thread limit. The value is set to None by
             default where no limit is set.
      :return: a context manager.
      """
      return ForecasterContextManager(self, thread_num, optimize=False)
  
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 6 for calling the AutoformerForecaster obj's underlying member method code with example usage: obj.get_context(thread_num) and returns: ForecasterContextManager