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 new run and possibly sets it as the active run.
  
  Args:
      tags: Tags to set for the new run.
      set_active: Whether to set the new run as the active run.
          If an active run already exists, then that run is returned.
  
  Returns:
      The newly created MLflow run.
docstr_tok: ["Starts","a","new","run","and","possibly","sets","it","as","the","active","run",".","Args",":","tags",":","Tags","to","set","for","the","new","run",".","set_active",":","Whether","to","set","the","new","run","as","the","active","run",".","If","an","active","run","already","exists",",","then","that","run","is","returned",".","Returns",":","The","newly","created","MLflow","run","."]
code: def start_run(
      self,
      run_name: Optional[str] = None,
      tags: Optional[Dict] = None,
      set_active: bool = False,
  ) -> "Run":
      """Starts a new run and possibly sets it as the active run.
  
      Args:
          tags: Tags to set for the new run.
          set_active: Whether to set the new run as the active run.
              If an active run already exists, then that run is returned.
  
      Returns:
          The newly created MLflow run.
      """
      import mlflow
      from mlflow.utils.mlflow_tags import MLFLOW_RUN_NAME
  
      if tags is None:
          tags = {}
  
      if set_active:
          return self._start_active_run(run_name=run_name, tags=tags)
  
      client = self._get_client()
      # If `mlflow==1.30.0` and we don't use `run_name`, then MLflow might error. For
      # more information, see #29749.
      if version.parse(mlflow.__version__) >= version.parse("1.30.0"):
          run = client.create_run(
              run_name=run_name,
              experiment_id=self.experiment_id,
              tags=tags,
          )
      else:
          tags[MLFLOW_RUN_NAME] = run_name
          run = client.create_run(
              experiment_id=self.experiment_id, tags=tags
          )
  
      return run
  
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 5 for calling the _MLflowLoggerUtil obj's underlying member method code with example usage: obj.start_run(run_name, tags, set_active) and returns: run, self