docs: def start_new(cls, tuner, inputs):
          """Create a new Amazon SageMaker hyperparameter tuning job from the HyperparameterTuner.
  
          Args:
              tuner (sagemaker.tuner.HyperparameterTuner): HyperparameterTuner object created by the user.
              inputs (str): Parameters used when called :meth:`~sagemaker.estimator.EstimatorBase.fit`.
  
          Returns:
              sagemaker.tuner._TuningJob: Constructed object that captures all information about the started job.
          """
          config = _Job._load_config(inputs, tuner.estimator)
  
          warm_start_config_req = None
          if tuner.warm_start_config:
              warm_start_config_req = tuner.warm_start_config.to_input_req()
  
          tuner_args = config.copy()
  
          tuner_args['job_name'] = tuner._current_job_name
          tuner_args['strategy'] = tuner.strategy
          tuner_args['objective_type'] = tuner.objective_type
          tuner_args['objective_metric_name'] = tuner.objective_metric_name
          tuner_args['max_jobs'] = tuner.max_jobs
          tuner_args['max_parallel_jobs'] = tuner.max_parallel_jobs
          tuner_args['parameter_ranges'] = tuner.hyperparameter_ranges()
          tuner_args['static_hyperparameters'] = tuner.static_hyperparameters
          tuner_args['input_mode'] = tuner.estimator.input_mode
          tuner_args['metric_definitions'] = tuner.metric_definitions
          tuner_args['tags'] = tuner.tags
          tuner_args['warm_start_config'] = warm_start_config_req
          tuner_args['early_stopping_type'] = tuner.early_stopping_type
  
          if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
              tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
          else:
              tuner_args['image'] = tuner.estimator.train_image()
  
          tuner_args['enable_network_isolation'] = tuner.estimator.enable_network_isolation()
          tuner_args['encrypt_inter_container_traffic'] = \
              tuner.estimator.encrypt_inter_container_traffic
  
          tuner.estimator.sagemaker_session.tune(**tuner_args)
  
          return cls(tuner.sagemaker_session, tuner._current_job_name)