docs: def create_training_job(self, TrainingJobName, AlgorithmSpecification, OutputDataConfig,
                              ResourceConfig, InputDataConfig=None, **kwargs):
          """
          Create a training job in Local Mode
          Args:
              TrainingJobName (str): local training job name.
              AlgorithmSpecification (dict): Identifies the training algorithm to use.
              InputDataConfig (dict): Describes the training dataset and the location where it is stored.
              OutputDataConfig (dict): Identifies the location where you want to save the results of model training.
              ResourceConfig (dict): Identifies the resources to use for local model traininig.
              HyperParameters (dict) [optional]: Specifies these algorithm-specific parameters to influence the quality of
                  the final model.
          """
          InputDataConfig = InputDataConfig or {}
          container = _SageMakerContainer(ResourceConfig['InstanceType'], ResourceConfig['InstanceCount'],
                                          AlgorithmSpecification['TrainingImage'], self.sagemaker_session)
          training_job = _LocalTrainingJob(container)
          hyperparameters = kwargs['HyperParameters'] if 'HyperParameters' in kwargs else {}
          training_job.start(InputDataConfig, OutputDataConfig, hyperparameters, TrainingJobName)
  
          LocalSagemakerClient._training_jobs[TrainingJobName] = training_job