code: def fit_predict_try_except_decorator(
      ta: Callable,
      queue: multiprocessing.Queue,
      cost_for_crash: float,
      **kwargs: Any,
  ) -> None:
      try:
          return ta(queue=queue, **kwargs)
      except Exception as e:
          if isinstance(e, (MemoryError, pynisher.TimeoutException)):
              # Re-raise the memory error to let the pynisher handle that correctly
              raise e
  
          exception_traceback = traceback.format_exc()
          error_message = repr(e)
  
          # Printing stuff to stdout just in case the queue doesn't work,
          # which happened with the
          # following traceback:
          #     File "auto-sklearn/autosklearn/evaluation/__init__.py", line 29, in fit_predict_try_except_decorator  # noqa E501
          #     return ta(queue=queue, **kwargs)
          #     File "auto-sklearn/autosklearn/evaluation/train_evaluator.py", line 1067, in eval_holdout  # noqa E501
          #     evaluator.fit_predict_and_loss(iterative=iterative)
          #     File "auto-sklearn/autosklearn/evaluation/train_evaluator.py", line 616, in fit_predict_and_loss,  # noqa E501
          #     status=status
          #     File "auto-sklearn/autosklearn/evaluation/abstract_evaluator.py", line 320, in finish_up  # noqa E501
          #     self.queue.put(return_value_dict)
          #     File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/multiprocessing/queues.py", line 87, in put  # noqa E501
          #     self._start_thread()
          #     File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/multiprocessing/queues.py", line 170, in _start_thread  # noqa E501
          #     self._thread.start()
          #     File "miniconda/3-4.5.4/envs/autosklearn/lib/python3.7/threading.py", line 847, in start  # noqa E501
          #     RuntimeError: can't start new thread
          print(
              "Exception handling in `fit_predict_try_except_decorator`: "
              "traceback: %s \nerror message: %s"
              % (exception_traceback, error_message)
          )
  
          queue.put(
              {
                  "loss": cost_for_crash,
                  "additional_run_info": {
                      "traceback": exception_traceback,
                      "error": error_message,
                  },
                  "status": StatusType.CRASHED,
                  "final_queue_element": True,
              },
              block=True,
          )
          queue.close()
  
imports: from __future__ import annotations
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Type, Union, cast
  import functools
  import json
  import logging
  import math
  import multiprocessing
  import time
  import traceback
  from queue import Empty
  import numpy
  import pynisher
  from ConfigSpace import Configuration
  from sklearn.model_selection._split import BaseCrossValidator, BaseShuffleSplit, _RepeatedSplits
  from smac.runhistory.runhistory import RunInfo, RunValue
  from smac.stats.stats import Stats
  from smac.tae import StatusType, TAEAbortException
  from smac.tae.execute_func import AbstractTAFunc
  import autosklearn.evaluation.test_evaluator
  import autosklearn.evaluation.train_evaluator
  import autosklearn.evaluation.util
  import autosklearn.pipeline.components
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.evaluation.train_evaluator import TYPE_ADDITIONAL_INFO
  from autosklearn.metrics import Scorer
  from autosklearn.util.logging_ import PickableLoggerAdapter, get_named_client_logger
  from autosklearn.util.parallel import preload_modules
label_desc: Use image node_id 1 for calling a global function with example usage: fit_predict_try_except_decorator(ta, queue, cost_for_crash) and returns: ta