### Description

A count of datasets that are currently registered/declared in an Airflow deployment.

### Use case/motivation

Would be nice to see how deployments are adopting datasets.

### 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._send_sla_callbacks_to_processor(dag)
        if callback:
            self.executor.send_callback(callback)
        else:
            self.log.debug("callback is empty")
    def _send_sla_callbacks_to_processor(self, dag: DAG) -> None:
        """Sends SLA Callbacks to DagFileProcessor if tasks have SLAs set and check_slas=True."""
        if not settings.CHECK_SLAS:
            return
        if not any(isinstance(task.sla, timedelta) for task in dag.tasks):
            self.log.debug("Skipping SLA check for %s because no tasks in DAG have SLAs", dag)
            return
        if not dag.timetable.periodic:
            self.log.debug("Skipping SLA check for %s because DAG is not scheduled", dag)
            return
        dag_model = DagModel.get_dagmodel(dag.dag_id)
        request = SlaCallbackRequest(
            full_filepath=dag.fileloc,
            dag_id=dag.dag_id,
            processor_subdir=dag_model.processor_subdir,
        )
        self.executor.send_callback(request)
    @provide_session
    def _emit_pool_metrics(self, session: Session = NEW_SESSION) -> None: