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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 training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=None):
 """Export Airflow base training config from an estimator Args: estimator (sagemaker.estimator.EstimatorBase):
 The estimator to export training config from. Can be a BYO estimator, Framework estimator or Amazon algorithm estimator. inputs: Information about the training data. Please refer to the ``fit()`` method of the associated estimator, 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 training job name if needed. mini_batch_size (int):
 Specify this argument only when estimator is a built-in estimator of an Amazon algorithm. For other estimators, batch size should be specified in the estimator. Returns: dict: Training config that can be directly used by SageMakerTrainingOperator in Airflow. """

    default_bucket = estimator.sagemaker_session.default_bucket()
    s3_operations = {}

    if job_name is not None:
        estimator._current_job_name = job_name
    else:
        base_name = estimator.base_job_name or utils.base_name_from_image(estimator.train_image())
        estimator._current_job_name = utils.name_from_base(base_name)

    if estimator.output_path is None:
        estimator.output_path = 's3://{}/'.format(default_bucket)

    if isinstance(estimator, sagemaker.estimator.Framework):
        prepare_framework(estimator, s3_operations)

    elif isinstance(estimator, amazon_estimator.AmazonAlgorithmEstimatorBase):
        prepare_amazon_algorithm_estimator(estimator, inputs, mini_batch_size)
    job_config = job._Job._load_config(inputs, estimator, expand_role=False, validate_uri=False)

    train_config = {
        'AlgorithmSpecification': {
            'TrainingImage': estimator.train_image(),
            'TrainingInputMode': estimator.input_mode
        },
        'OutputDataConfig': job_config['output_config'],
        'StoppingCondition': job_config['stop_condition'],
        'ResourceConfig': job_config['resource_config'],
        'RoleArn': job_config['role'],
    }

    if job_config['input_config'] is not None:
        train_config['InputDataConfig'] = job_config['input_config']

    if job_config['vpc_config'] is not None:
        train_config['VpcConfig'] = job_config['vpc_config']

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

    if hyperparameters and len(hyperparameters) > 0:
        train_config['HyperParameters'] = hyperparameters

    if s3_operations:
        train_config['S3Operations'] = s3_operations

    return train_config