class_docstr: Trainable that runs a user function reporting results.
  
  This mode of execution does not support checkpoint/restore.
code: def _create_checkpoint_dir(
      self, checkpoint_dir: Optional[str] = None
  ) -> Optional[str]:
      return None
  
imports: import inspect
  import logging
  import os
  from functools import partial
  from numbers import Number
  from typing import Any, Callable, Dict, Optional, Type
  from ray.air._internal.util import StartTraceback, RunnerThread
  import queue
  from ray.air.constants import _ERROR_FETCH_TIMEOUT
  import ray.train
  from ray.train._internal.checkpoint_manager import _TrainingResult
  from ray.train._internal.session import init_session, get_session, shutdown_session, _TrainSession, TrialInfo
  from ray.tune.execution.placement_groups import PlacementGroupFactory
  from ray.tune.result import DEFAULT_METRIC, RESULT_DUPLICATE, SHOULD_CHECKPOINT
  from ray.tune.trainable import Trainable
  from ray.tune.utils import _detect_checkpoint_function, _detect_config_single, _detect_reporter
  from ray.util.annotations import DeveloperAPI
label_desc: Use image node_id 7 for calling the FunctionTrainable obj's underlying member method code with example usage: obj._create_checkpoint_dir(checkpoint_dir) and returns: None