### 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)

"""Return OpenLineage data gathered from SageMaker's API response saved by training job."""
        from airflow.providers.openlineage.extractors import OperatorLineage
        inputs = []
        outputs = []
        try:
            for input_data in self.serialized_training_data["InputDataConfig"]:
                inputs.append(self.path_to_s3_dataset(input_data["DataSource"]["S3DataSource"]["S3Uri"]))
        except KeyError:
            self.log.exception("Issues extracting inputs.")
        try:
            outputs.append(
                self.path_to_s3_dataset(self.serialized_training_data["ModelArtifacts"]["S3ModelArtifacts"])
            )
        except KeyError:
            self.log.exception("Issues extracting inputs.")
        return OperatorLineage(inputs=inputs, outputs=outputs)
class SageMakerDeleteModelOperator(SageMakerBaseOperator):
    """
    Deletes a SageMaker model.
    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:SageMakerDeleteModelOperator`
    :param config: The configuration necessary to delete the model.
        For details of the configuration parameter see :py:meth:`SageMaker.Client.delete_model`
    :param aws_conn_id: The AWS connection ID to use.
    """
    def __init__(self, *, config: dict, aws_conn_id: str = DEFAULT_CONN_ID, **kwargs):