class_docstr: Wandb assumes that each trial's information should be logged from a
  separate process. We use Ray actors as forking multiprocessing
  processes is not supported by Ray and spawn processes run into pickling
  problems.
  
  We use a queue for the driver to communicate with the logging process.
  The queue accepts the following items:
  
  - If it's a dict, it is assumed to be a result and will be logged using
    ``wandb.log()``
  - If it's a checkpoint object, it will be saved using ``wandb.log_artifact()``.
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code: def _handle_checkpoint(self, checkpoint_path: str):
      artifact = self._wandb.Artifact(
          name=f"checkpoint_{self._trial_name}", type="model"
      )
      artifact.add_dir(checkpoint_path)
      self._wandb.log_artifact(artifact)
  
imports: import enum
  import os
  import pickle
  import urllib
  import warnings
  import numpy
  from numbers import Number
  import pyarrow.fs
  from types import ModuleType
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import ray
  from ray import logger
  from ray.air import session
  from ray.air._internal import usage
  from ray.air.util.node import _force_on_current_node
  from ray.tune.logger import LoggerCallback
  from ray.tune.utils import flatten_dict
  from ray.tune.experiment import Trial
  from ray.train._internal.syncer import DEFAULT_SYNC_TIMEOUT
  from ray._private.storage import _load_class
  from ray.util import PublicAPI
  from ray.util.queue import Queue
label_desc: Use image node_id 3 for calling the _WandbLoggingActor obj's underlying member method code with example usage: obj._handle_checkpoint(checkpoint_path) without return types