docstr: Transform a distributed forecaster to a local (non-distributed) one.
  
  you need to call .to_local() and transform the forecaster to a non-
  distributed one.
docstr_tok: ["Transform","a","distributed","forecaster","to","a","local","(","non-distributed",")","one",".","you","need","to","call",".to_local","(",")","and","transform","the","forecaster","to","a","non-","distributed","one","."]
code: def to_local(self):
      """
      Transform a distributed forecaster to a local (non-distributed) one.
  
      you need to call .to_local() and transform the forecaster to a non-
      distributed one.
  
      """
      if not self.distributed:
          warnings.warn("The forecaster has become local.")
  
      model = self.internal.get_model()
      self.internal.shutdown()
      self.internal = model
  
      self.distributed = False
  
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 6 for calling the BaseTF2Forecaster obj's underlying member method code with example usage: obj.to_local() without return types