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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 deploy_config(model, initial_instance_count, instance_type, endpoint_name=None, tags=None):
 """Export Airflow deploy config from a SageMaker model Args: model (sagemaker.model.Model):
 The SageMaker model to export the Airflow config from. instance_type (str):
 The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'. initial_instance_count (int):
 The initial number of instances to run in the ``Endpoint`` created from this ``Model``. endpoint_name (str):
 The name of the endpoint to create (default: None). If not specified, a unique endpoint name will be created. tags (list[dict]):
 List of tags for labeling a training job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html. Returns: dict: Deploy config that can be directly used by SageMakerEndpointOperator in Airflow. """

    model_base_config = model_config(instance_type, model)

    production_variant = sagemaker.production_variant(model.name, instance_type, initial_instance_count)
    name = model.name
    config_options = {'EndpointConfigName': name, 'ProductionVariants': [production_variant]}
    if tags is not None:
        config_options['Tags'] = tags

    endpoint_name = endpoint_name or name
    endpoint_base_config = {
        'EndpointName': endpoint_name,
        'EndpointConfigName': name
    }

    config = {
        'Model': model_base_config,
        'EndpointConfig': config_options,
        'Endpoint': endpoint_base_config
    }

    # if there is s3 operations needed for model, move it to root level of config
    s3_operations = model_base_config.pop('S3Operations', None)
    if s3_operations is not None:
        config['S3Operations'] = s3_operations

    return config