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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)