code: def deploy_config_from_estimator(estimator, task_id, task_type, initial_instance_count, instance_type,
                                   model_name=None, endpoint_name=None, tags=None, **kwargs):
      
      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
docs: 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.