### Description

Today, Amazon provider package supports SageMaker operators for a few operations, like training, tuning, pipelines, but it lacks the support for SageMaker Notebook instances. Boto3 provides necessary APIs to [create notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/create_notebook_instance.html), [start notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/start_notebook_instance.html), [stop network instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/stop_notebook_instance.html) and [delete notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/delete_notebook_instance.html). Leveraging these APIs, we should add new operators to SageMaker set under Amazon provider. At the same time, having a sensor (synchronous as well as deferrable) for notebook instance execution that utilizes [describe notebook instance](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/describe_notebook_instance.html) and waits for Stopped/Failed status would help with observability of the execution.

### Use case/motivation

Data Scientists are orchestrating ML use cases via Apache Airflow. A key component of ML use cases is running Jupyter Notebook on SageMaker. Having built-in operators and sensors would make it easy for Airflow users to run Notebook instances on SageMaker.

### Related issues

_No response_

### Are you willing to submit a PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""Expand an IAM role name into an ARN."""
        if "TrainingJobDefinition" in self.config:
            config = self.config["TrainingJobDefinition"]
            if "RoleArn" in config:
                hook = AwsBaseHook(self.aws_conn_id, client_type="iam")
                config["RoleArn"] = hook.expand_role(config["RoleArn"])
    def _create_integer_fields(self) -> None:
        """Set fields which should be cast to integers."""
        self.integer_fields: list[list[str]] = [
            ["HyperParameterTuningJobConfig", "ResourceLimits", "MaxNumberOfTrainingJobs"],
            ["HyperParameterTuningJobConfig", "ResourceLimits", "MaxParallelTrainingJobs"],
            ["TrainingJobDefinition", "ResourceConfig", "InstanceCount"],
            ["TrainingJobDefinition", "ResourceConfig", "VolumeSizeInGB"],
            ["TrainingJobDefinition", "StoppingCondition", "MaxRuntimeInSeconds"],
        ]
    def execute(self, context: Context) -> dict:
        self.preprocess_config()
        self.log.info(
            "Creating SageMaker Hyper-Parameter Tuning Job %s", self.config["HyperParameterTuningJobName"]
        )
        response = self.hook.create_tuning_job(
            self.config,
            wait_for_completion=False,  
            check_interval=self.check_interval,
            max_ingestion_time=self.max_ingestion_time,
        )
        if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
            raise AirflowException(f"Sagemaker Tuning Job creation failed: {response}")
        if self.deferrable:
            self.defer(