code: def test_tag_trial_user_attrs(
      tmpdir: py.path.local, tag_trial_user_attrs: bool
  ) -> None:
      tracking_uri = f"file:{tmpdir}"
      study_name = "my_study"
      n_trials = 3
  
      mlflc = MLflowCallback(
          tracking_uri=tracking_uri,
          tag_trial_user_attrs=tag_trial_user_attrs,
      )
      study = optuna.create_study(study_name=study_name)
      study.optimize(
          _objective_func, n_trials=n_trials, callbacks=[mlflc]
      )
  
      mlfl_client = MlflowClient(tracking_uri)
      experiment = mlfl_client.search_experiments()[0]
      runs = mlfl_client.search_runs([experiment.experiment_id])
  
      if tag_trial_user_attrs:
          assert all(
              (r.data.tags["my_user_attr"] == "my_user_attr_value")
              for r in runs
          )
      else:
          assert all(("my_user_attr" not in r.data.tags) for r in runs)
  
imports: from typing import Callable
  from typing import List
  from typing import Optional
  from typing import Tuple
  from typing import Union
  import numpy
  import py
  import pytest
  import optuna
  from optuna._imports import try_import
  from optuna.integration.mlflow import MLflowCallback
label_desc: Use image node_id 16 for calling a global function with example usage: test_tag_trial_user_attrs(tmpdir, tag_trial_user_attrs) without return types