code: def mlflow_args(
      caliban_config: Dict[str, Any],
      experiment_name: str,
      index: int,
      tags: Dict[str, Any],
  ) -> List[str]:
      """returns mlflow args for caliban launcher
      Args:
  
      caliban_config: caliban configuration dict
      experiment: experiment object
      index: job index
      tags: dictionary of tags to pass to mlflow
  
      Returns:
      mlflow args list
      """
  
      if caliban_config.get("mlflow_config") is None:
          return []
  
      env = {f"ENVVAR_{k}": v for k, v in tags.items()}
      env["MLFLOW_EXPERIMENT_NAME"] = experiment_name
      env["MLFLOW_RUN_NAME"] = _mlflow_job_name(index=index)
  
      return ["--caliban_config", json.dumps({"env": env})]
  
file_path: caliban/caliban/util/metrics.py
imports: import datetime
  import json
  import os
  import caliban.util
  import caliban.history.types
  from contextlib import contextmanager
  from typing import Dict, Any, List, Optional
label_desc: Use image node_id 7 for calling a global function with example usage: mlflow_args(caliban_config, experiment_name, index, tags) and returns: list, list