Add describe_hyper_parameter_tuning_job() to the SageMaker Session class

[alex23lemm]

Is your feature request related to a problem? Please describe.
A SageMaker Session object comes with the following methods which allow me to retrieve the status of training, batch transform, processing and AutoML jobs: describe_training_job(), describe_transform_job(), describe_processing_job(), describe_auto_ml_job(). Examples:

import sagemaker

session = sagemaker.Session()
session.describe_training_job("[YOUR_JOB_NAME]")["TrainingJobStatus"]
session.describe_transform_job("[YOUR_JOB_NAME]")["TransformJobStatus"]
session.describe_processing_job("[YOUR_JOB_NAME]")["ProcessingJobStatus"]
session.describe_auto_ml_job("[YOUR_JOB_NAME]")["AutoMLJobStatus"]

However, if I like to get the status of a hyperparameter tuning job in the same workflow, I need to use one of the following two options:

Option 1: Use the following two lines of code

tuning_job_results = sagemaker.HyperparameterTuningJobAnalytics("[YOUR_JOB_NAME]")
tuning_job_results.description()["HyperParameterTuningJobStatus"]

Option 2: Create an extra low-level SageMaker client using boto3:

import boto3

sm = boto3.client("sagemaker")
sm.describe_hyper_parameter_tuning_job(HyperParameterTuningJobName = "[YOUR_JOB_NAME]")["HyperParameterTuningJobStatus"]

Describe the solution you'd like
Even though the above options work, it could become much more concise and smoother by adding a describe_hyper_parameter_tuning_job() method to the SageMaker Session class. This would harmonize the family of describe_[...]_job() functions.

[laurenyu]

#1594 has been merged. should get released later today

[laurenyu]

released in https://github.com/aws/sagemaker-python-sdk/releases/tag/v1.65.0