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Concept: In Azure Machine Learning, you can build real-time inferencing solutions by deploying a model as a service, hosted on a containerized platform such as Azure Kubernetes Services (AKS). Deploying a model involves multiple components, including: The trained model The runtime environment configuration The scoring script The container image The container host Because deployment issues and errors can be complex to troubleshoot, understanding key debugging methods is essential. Code Review: The following Python code snippet interacts with an AKS-deployed web service: python from azureml . core . webservice import AksWebservice service = AksWebservice ( name = 'classifier-service' , workspace = ws ) print ( service . state ) Question: What does this code do in the context of troubleshooting a deployed Azure ML service?