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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_from_estimator(estimator, task_id, task_type, initial_instance_count, instance_type, model_name=None, endpoint_name=None, tags=None, **kwargs):
 """Export Airflow deploy config from a SageMaker estimator Args: estimator (sagemaker.model.EstimatorBase):
 The SageMaker estimator to export Airflow config from. It has to be an estimator associated with a training job. task_id (str):
 The task id of any airflow.contrib.operators.SageMakerTrainingOperator or airflow.contrib.operators.SageMakerTuningOperator that generates training jobs in the DAG. The endpoint config is built based on the training job generated in this operator. task_type (str):
 Whether the task is from SageMakerTrainingOperator or SageMakerTuningOperator. Values can be 'training', 'tuning' or None (which means training job is not from any task). initial_instance_count (int):
 Minimum number of EC2 instances to deploy to an endpoint for prediction. instance_type (str):
 Type of EC2 instance to deploy to an endpoint for prediction, for example, 'ml.c4.xlarge'. model_name (str):
 Name to use for creating an Amazon SageMaker model. If not specified, one will be generated. endpoint_name (str):
 Name to use for creating an Amazon SageMaker endpoint. If not specified, the name of the SageMaker model is used. 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. **kwargs: Passed to invocation of ``create_model()``. Implementations may customize ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy. For more, see the implementation docs. Returns: dict: Deploy config that can be directly used by SageMakerEndpointOperator in Airflow. """

    update_estimator_from_task(estimator, task_id, task_type)
    model = estimator.create_model(**kwargs)
    model.name = model_name
    config = deploy_config(model, initial_instance_count, instance_type, endpoint_name, tags)
    return config