<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def transform_config(transformer, data, data_type='S3Prefix', content_type=None, compression_type=None, split_type=None, job_name=None):
 """Export Airflow transform config from a SageMaker transformer Args: transformer (sagemaker.transformer.Transformer):
 The SageMaker transformer to export Airflow config from. data (str):
 Input data location in S3. data_type (str):
 What the S3 location defines (default: 'S3Prefix'). Valid values: * 'S3Prefix' - the S3 URI defines a key name prefix. All objects with this prefix will be used as inputs for the transform job. * 'ManifestFile' - the S3 URI points to a single manifest file listing each S3 object to use as an input for the transform job. content_type (str):
 MIME type of the input data (default: None). compression_type (str):
 Compression type of the input data, if compressed (default: None). Valid values: 'Gzip', None. split_type (str):
 The record delimiter for the input object (default: 'None'). Valid values: 'None', 'Line', 'RecordIO', and 'TFRecord'. job_name (str):
 job name (default: None). If not specified, one will be generated. Returns: dict: Transform config that can be directly used by SageMakerTransformOperator in Airflow. """

    if job_name is not None:
        transformer._current_job_name = job_name
    else:
        base_name = transformer.base_transform_job_name
        transformer._current_job_name = utils.name_from_base(base_name) \
            if base_name is not None else transformer.model_name

    if transformer.output_path is None:
        transformer.output_path = 's3://{}/{}'.format(
            transformer.sagemaker_session.default_bucket(), transformer._current_job_name)

    job_config = sagemaker.transformer._TransformJob._load_config(
        data, data_type, content_type, compression_type, split_type, transformer)

    config = {
        'TransformJobName': transformer._current_job_name,
        'ModelName': transformer.model_name,
        'TransformInput': job_config['input_config'],
        'TransformOutput': job_config['output_config'],
        'TransformResources': job_config['resource_config'],
    }

    if transformer.strategy is not None:
        config['BatchStrategy'] = transformer.strategy

    if transformer.max_concurrent_transforms is not None:
        config['MaxConcurrentTransforms'] = transformer.max_concurrent_transforms

    if transformer.max_payload is not None:
        config['MaxPayloadInMB'] = transformer.max_payload

    if transformer.env is not None:
        config['Environment'] = transformer.env

    if transformer.tags is not None:
        config['Tags'] = transformer.tags

    return config