### 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)

if event["status"] != "success":
            raise AirflowException(f"Error while running job: {event}")
        endpoint_info = self.config.get("Endpoint", self.config)
        return {
            "EndpointConfig": serialize(
                self.hook.describe_endpoint_config(endpoint_info["EndpointConfigName"])
            ),
            "Endpoint": serialize(self.hook.describe_endpoint(endpoint_info["EndpointName"])),
        }
class SageMakerTransformOperator(SageMakerBaseOperator):
    """
    Starts a transform job.
    A transform job uses a trained model to get inferences on a dataset
    and saves these results to an Amazon S3 location that you specify.
    .. seealso::
        For more information on how to use this operator, take a look at the guide: