class_docstr: A class used to export/load remote functions and actors.
  Attributes:
      _worker: The associated worker that this manager related.
      _functions_to_export: The remote functions to export when
          the worker gets connected.
      _actors_to_export: The actors to export when the worker gets
          connected.
      _function_execution_info: The function_id
          and execution_info.
      _num_task_executions: The function
          execution times.
      imported_actor_classes: The set of actor classes keys (format:
          ActorClass:function_id) that are already in GCS.
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docstr: Load actor class from GCS.
code: def _load_actor_class_from_gcs(
      self, job_id, actor_creation_function_descriptor
  ):
      """Load actor class from GCS."""
      key = make_function_table_key(
          b"ActorClass",
          job_id,
          actor_creation_function_descriptor.function_id.binary(),
      )
  
      # Fetch raw data from GCS.
      vals = self._worker.gcs_client.internal_kv_get(
          key, KV_NAMESPACE_FUNCTION_TABLE
      )
      fields = [
          "job_id",
          "class_name",
          "module",
          "class",
          "actor_method_names",
      ]
      if vals is None:
          vals = {}
      else:
          vals = pickle.loads(vals)
      (
          job_id_str,
          class_name,
          module,
          pickled_class,
          actor_method_names,
      ) = (vals.get(field) for field in fields)
  
      class_name = ensure_str(class_name)
      module_name = ensure_str(module)
      job_id = ray.JobID(job_id_str)
      actor_method_names = json.loads(ensure_str(actor_method_names))
  
      actor_class = None
      try:
          with self.lock:
              actor_class = pickle.loads(pickled_class)
      except Exception:
          logger.debug("Failed to load actor class %s.", class_name)
          # If an exception was thrown when the actor was imported, we record
          # the traceback and notify the scheduler of the failure.
          traceback_str = format_error_message(traceback.format_exc())
          # The actor class failed to be unpickled, create a fake actor
          # class instead (just to produce error messages and to prevent
          # the driver from hanging).
          actor_class = self._create_fake_actor_class(
              class_name, actor_method_names, traceback_str
          )
  
      # The below line is necessary. Because in the driver process,
      # if the function is defined in the file where the python script
      # was started from, its module is `__main__`.
      # However in the worker process, the `__main__` module is a
      # different module, which is `default_worker.py`
      actor_class.__module__ = module_name
      return actor_class
  
imports: import dis
  import sys
  import hashlib
  import importlib
  import inspect
  import json
  import logging
  import os
  import threading
  import time
  import traceback
  from collections import defaultdict, namedtuple
  from typing import Optional, Callable
  import ray
  import ray._private.profiling
  from ray import cloudpickle
  from ray._private import ray_constants
  from ray._private.inspect_util import is_class_method, is_function_or_method, is_static_method
  from ray._private.ray_constants import KV_NAMESPACE_FUNCTION_TABLE
  from ray._private.utils import check_oversized_function, ensure_str, format_error_message
  from ray._private.serialization import pickle_dumps
  from ray._raylet import JobID, PythonFunctionDescriptor, WORKER_PROCESS_SETUP_HOOK_KEY_NAME_GCS
label_desc: Use image node_id 18 for calling the FunctionActorManager obj's underlying member method code with example usage: obj._load_actor_class_from_gcs(job_id, actor_creation_function_descriptor) and returns: actor_class