code: def test_remote_trial_dir_with_reuse_actors(
      trainable, ray_start_2_cpus, monkeypatch, tmp_path
  ):
      """Check that the trainable has its remote directory set to the right
      location, when new trials get swapped in on actor reuse.
      Each trial runs for 2 iterations, with checkpoint_frequency=1, so each
      remote trial dir should have 2 checkpoints.
      """
      monkeypatch.setenv("RAY_AIR_LOCAL_CACHE_DIR", str(tmp_path))
      tmp_target = str(tmp_path / "upload_dir")
      exp_name = "remote_trial_dir_update_on_actor_reuse"
  
      def get_remote_trial_dir(trial_id: int):
          return os.path.join(tmp_target, exp_name, str(trial_id))
  
      analysis = _run_trials_with_frequent_pauses(
          trainable,
          reuse=True,
          max_concurrent_trials=2,
          name=exp_name,
          storage_path=f"file://{tmp_target}",
          trial_dirname_creator=lambda t: str(t.config.get("id")),
          checkpoint_config=CheckpointConfig(
              checkpoint_frequency=1
              if inspect.isclass(trainable)
              else 0
          ),
      )
      result_grid = ResultGrid(analysis)
      assert not result_grid.errors
  
      # Check that each remote trial dir has 2 checkpoints.
      for result in result_grid:
          trial_id = result.config["id"]
          remote_dir = get_remote_trial_dir(trial_id)
          num_checkpoints = len(
              [
                  file
                  for file in os.listdir(remote_dir)
                  if file.startswith("checkpoint_")
              ]
          )
          assert num_checkpoints == 2
  
imports: import inspect
  import os
  from pathlib import Path
  import tempfile
  from typing import Callable
  import pytest
  import sys
  import time
  import ray
  from ray import train, tune, logger
  from ray.train import CheckpointConfig
  from ray.tune import Trainable, run_experiments, register_trainable
  from ray.tune.error import TuneError
  from ray.tune.result_grid import ResultGrid
  from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
  from ray.tune.tune import _check_mixin
  from ray.train.tests.util import create_dict_checkpoint, load_dict_checkpoint
label_desc: Use image node_id 18 for calling a global function with example usage: test_remote_trial_dir_with_reuse_actors(trainable, ray_start_2_cpus, monkeypatch, tmp_path) and returns: os