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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_config(estimator, inputs=None, job_name=None, mini_batch_size=None):
 """Export Airflow 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. """


    train_config = training_base_config(estimator, inputs, job_name, mini_batch_size)

    train_config['TrainingJobName'] = estimator._current_job_name

    if estimator.tags is not None:
        train_config['Tags'] = estimator.tags

    return train_config