code: def test_tag_always_logged(tmpdir: py.path.local) -> None:
      tracking_uri = f"file:{tmpdir}"
      study_name = "my_study"
      n_trials = 3
  
      mlflc = MLflowCallback(tracking_uri=tracking_uri)
      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])
  
      assert all((r.data.tags["direction"] == "MINIMIZE") for r in runs)
      assert all((r.data.tags["state"] == "COMPLETE") 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 14 for calling a global function with example usage: test_tag_always_logged(tmpdir) without return types