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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, estimator, inputs):
 """Create a new Amazon SageMaker training job from the estimator. Args: estimator (sagemaker.estimator.EstimatorBase):
 Estimator object created by the user. inputs (str):
 Parameters used when called :meth:`~sagemaker.estimator.EstimatorBase.fit`. Returns: sagemaker.estimator._TrainingJob: Constructed object that captures all information about the started training job. """


        local_mode = estimator.sagemaker_session.local_mode
        model_uri = estimator.model_uri

        # Allow file:// input only in local mode
        if cls._is_local_channel(inputs) or cls._is_local_channel(model_uri):
            if not local_mode:
                raise ValueError('File URIs are supported in local mode only. Please use a S3 URI instead.')

        config = _Job._load_config(inputs, estimator)

        if estimator.hyperparameters() is not None:
            hyperparameters = {str(k): str(v) for (k, v) in estimator.hyperparameters().items()}

        train_args = config.copy()
        train_args['input_mode'] = estimator.input_mode
        train_args['job_name'] = estimator._current_job_name
        train_args['hyperparameters'] = hyperparameters
        train_args['tags'] = estimator.tags
        train_args['metric_definitions'] = estimator.metric_definitions

        if estimator.enable_network_isolation():
            train_args['enable_network_isolation'] = True

        if estimator.encrypt_inter_container_traffic:
            train_args['encrypt_inter_container_traffic'] = True

        if isinstance(estimator, sagemaker.algorithm.AlgorithmEstimator):
            train_args['algorithm_arn'] = estimator.algorithm_arn
        else:
            train_args['image'] = estimator.train_image()

        estimator.sagemaker_session.train(**train_args)

        return cls(estimator.sagemaker_session, estimator._current_job_name)