### 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)

self,
        *,
        config: dict,
        aws_conn_id: str = DEFAULT_CONN_ID,
        **kwargs,
    ):
        super().__init__(config=config, aws_conn_id=aws_conn_id, **kwargs)
    def _create_integer_fields(self) -> None:
        """Set fields which should be cast to integers."""
        self.integer_fields: list[list[str]] = [["ProductionVariants", "InitialInstanceCount"]]
    def execute(self, context: Context) -> dict:
        self.preprocess_config()
        self.log.info("Creating SageMaker Endpoint Config %s.", self.config["EndpointConfigName"])
        response = self.hook.create_endpoint_config(self.config)
        if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
            raise AirflowException(f"Sagemaker endpoint config creation failed: {response}")
        else:
            return {
                "EndpointConfig": serialize(
                    self.hook.describe_endpoint_config(self.config["EndpointConfigName"])
                )
            }
class SageMakerEndpointOperator(SageMakerBaseOperator):