code: def _maybe_set_run_terminated(active_run, status):
      """
      If the passed-in active run is defined and still running (i.e. hasn't already been terminated
      within user code), mark it as terminated with the passed-in status.
      """
      if active_run is None:
          return
      run_id = active_run.info.run_id
      cur_status = tracking.MlflowClient().get_run(run_id).info.status
      if RunStatus.is_terminated(cur_status):
          return
      tracking.MlflowClient().set_terminated(run_id, status)
  
imports: import json
  import logging
  import os
  import yaml
  import mlflow.projects.databricks
  import mlflow.utils.uri
  from mlflow import tracking
  from mlflow.entities import RunStatus
  from mlflow.exceptions import ExecutionException, MlflowException
  from mlflow.projects.backend import loader
  from mlflow.projects.submitted_run import SubmittedRun
  from mlflow.projects.utils import MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG, PROJECT_BUILD_IMAGE, PROJECT_DOCKER_ARGS, PROJECT_DOCKER_AUTH, PROJECT_ENV_MANAGER, PROJECT_STORAGE_DIR, PROJECT_SYNCHRONOUS, fetch_and_validate_project, get_entry_point_command, get_or_create_run, get_run_env_vars, load_project
  from mlflow.tracking.fluent import _get_experiment_id
  from mlflow.utils import env_manager
  from mlflow.utils.mlflow_tags import MLFLOW_DOCKER_IMAGE_ID, MLFLOW_PROJECT_BACKEND, MLFLOW_PROJECT_ENV, MLFLOW_RUN_NAME
label_desc: Use image node_id 5 for calling a global function with example usage: _maybe_set_run_terminated(active_run, status) without return types