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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 tuning_config(tuner, inputs, job_name=None):
 """Export Airflow tuning config from an estimator Args: tuner (sagemaker.tuner.HyperparameterTuner):
 The tuner to export tuning config from. inputs: Information about the training data. Please refer to the ``fit()`` method of the associated estimator in the tuner, as this can take any of the following forms: * (str) - The S3 location where training data is saved. * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for training data, you can specify a dict mapping channel names to strings or :func:`~sagemaker.session.s3_input` objects. * (sagemaker.session.s3_input) - Channel configuration for S3 data sources that can provide additional information about the training dataset. See :func:`sagemaker.session.s3_input` for full details. * (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of Amazon :class:~`Record` objects serialized and stored in S3. For use with an estimator for an Amazon algorithm. * (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of :class:~`sagemaker.amazon.amazon_estimator.RecordSet` objects, where each instance is a different channel of training data. job_name (str):
 Specify a tuning job name if needed. Returns: dict: Tuning config that can be directly used by SageMakerTuningOperator in Airflow. """

    train_config = training_base_config(tuner.estimator, inputs)
    hyperparameters = train_config.pop('HyperParameters', None)
    s3_operations = train_config.pop('S3Operations', None)

    if hyperparameters and len(hyperparameters) > 0:
        tuner.static_hyperparameters = \
            {utils.to_str(k): utils.to_str(v) for (k, v) in hyperparameters.items()}

    if job_name is not None:
        tuner._current_job_name = job_name
    else:
        base_name = tuner.base_tuning_job_name or utils.base_name_from_image(tuner.estimator.train_image())
        tuner._current_job_name = utils.name_from_base(base_name, tuner.TUNING_JOB_NAME_MAX_LENGTH, True)

    for hyperparameter_name in tuner._hyperparameter_ranges.keys():
        tuner.static_hyperparameters.pop(hyperparameter_name, None)

    train_config['StaticHyperParameters'] = tuner.static_hyperparameters

    tune_config = {
        'HyperParameterTuningJobName': tuner._current_job_name,
        'HyperParameterTuningJobConfig': {
            'Strategy': tuner.strategy,
            'HyperParameterTuningJobObjective': {
                'Type': tuner.objective_type,
                'MetricName': tuner.objective_metric_name,
            },
            'ResourceLimits': {
                'MaxNumberOfTrainingJobs': tuner.max_jobs,
                'MaxParallelTrainingJobs': tuner.max_parallel_jobs,
            },
            'ParameterRanges': tuner.hyperparameter_ranges(),
        },
        'TrainingJobDefinition': train_config
    }

    if tuner.metric_definitions is not None:
        tune_config['TrainingJobDefinition']['AlgorithmSpecification']['MetricDefinitions'] = \
            tuner.metric_definitions

    if tuner.tags is not None:
        tune_config['Tags'] = tuner.tags

    if s3_operations is not None:
        tune_config['S3Operations'] = s3_operations

    return tune_config