class_docstr: Util class for setting up and logging to MLflow.
  
  Use this util for any library that needs MLflow logging/tracking logic
  such as Ray Tune or Ray Train.
class_docstr_tok: ["Util","class","for","setting","up","and","logging","to","MLflow",".","Use","this","util","for","any","library","that","needs","MLflow","logging\/tracking","logic","such","as","Ray","Tune","or","Ray","Train","."]
docstr: Starts a run and sets it as the active run if one does not exist.
  
  If an active run already exists, then returns it.
docstr_tok: ["Starts","a","run","and","sets","it","as","the","active","run","if","one","does","not","exist",".","If","an","active","run","already","exists",",","then","returns","it","."]
code: def _start_active_run(
      self, run_name: Optional[str] = None, tags: Optional[Dict] = None
  ) -> "Run":
      """Starts a run and sets it as the active run if one does not exist.
  
      If an active run already exists, then returns it.
      """
      active_run = self._mlflow.active_run()
      if active_run:
          return active_run
  
      return self._mlflow.start_run(
          run_name=run_name, experiment_id=self.experiment_id, tags=tags
      )
  
file_path: ray/python/ray/air/_internal/mlflow.py
imports: import logging
  import os
  from packaging import version
  from copy import deepcopy
  from typing import TYPE_CHECKING, Dict, Optional
  from ray._private.dict import flatten_dict
label_desc: Use image node_id 6 for calling the _MLflowLoggerUtil obj's underlying member method code with example usage: obj._start_active_run(run_name, tags) and returns: self, active_run