code: def run(
      self,
      config: Configuration,
      instance: Optional[str] = None,
      cutoff: Optional[float] = None,
      seed: int = 12345,
      budget: float = 0.0,
      instance_specific: Optional[str] = None,
  ) -> Tuple[
      StatusType,
      float | list[float],
      float,
      Dict[str, Union[int, float, str, Dict, List, Tuple]],
  ]:
      # Additional information of each of the tae executions
      # Defined upfront for mypy
      additional_run_info: TYPE_ADDITIONAL_INFO = {}
  
      context = multiprocessing.get_context(self.pynisher_context)
      preload_modules(context)
      queue = context.Queue()
  
      if not (instance_specific is None or instance_specific == "0"):
          raise ValueError(instance_specific)
  
      init_params = {"instance": instance}
      if self.init_params is not None:
          init_params.update(self.init_params)
  
      if self.port is None:
          logger: Union[
              logging.Logger, PickableLoggerAdapter
          ] = logging.getLogger("pynisher")
      else:
          logger = get_named_client_logger(
              name="pynisher",
              port=self.port,
          )
      arguments = dict(
          logger=logger,
          wall_time_in_s=cutoff,
          mem_in_mb=self.memory_limit,
          capture_output=True,
          context=context,
      )
  
      if isinstance(config, int):
          num_run = self.initial_num_run
      else:
          num_run = config.config_id + self.initial_num_run
  
      obj_kwargs = dict(
          queue=queue,
          config=config,
          backend=self.backend,
          port=self.port,
          metrics=self.metrics,
          seed=self.autosklearn_seed,
          num_run=num_run,
          scoring_functions=self.scoring_functions,
          output_y_hat_optimization=self.output_y_hat_optimization,
          include=self.include,
          exclude=self.exclude,
          disable_file_output=self.disable_file_output,
          instance=instance,
          init_params=init_params,
          budget=budget,
          budget_type=self.budget_type,
          additional_components=autosklearn.pipeline.components.base._addons,
      )
  
      if self.resampling_strategy != "test":
          obj_kwargs["resampling_strategy"] = self.resampling_strategy
          obj_kwargs[
              "resampling_strategy_args"
          ] = self.resampling_strategy_args
  
      try:
          obj = pynisher.enforce_limits(**arguments)(self.ta)
          obj(**obj_kwargs)
      except Exception as e:
          exception_traceback = traceback.format_exc()
          error_message = repr(e)
          additional_run_info.update(
              {"traceback": exception_traceback, "error": error_message}
          )
          return (
              StatusType.CRASHED,
              self.worst_possible_result,
              0.0,
              additional_run_info,
          )
  
      if obj.exit_status in (
          pynisher.TimeoutException,
          pynisher.MemorylimitException,
      ):
          # Even if the pynisher thinks that a timeout or memout occured,
          # it can be that the target algorithm wrote something into the queue
          #  - then we treat it as a succesful run
          try:
              info = autosklearn.evaluation.util.read_queue(queue)
              result = info[-1]["loss"]
              status = info[-1]["status"]
              additional_run_info = info[-1]["additional_run_info"]
  
              if obj.stdout:
                  additional_run_info["subprocess_stdout"] = obj.stdout
              if obj.stderr:
                  additional_run_info["subprocess_stderr"] = obj.stderr
  
              if obj.exit_status is pynisher.TimeoutException:
                  additional_run_info[
                      "info"
                  ] = "Run stopped because of timeout."
              elif obj.exit_status is pynisher.MemorylimitException:
                  additional_run_info[
                      "info"
                  ] = "Run stopped because of memout."
  
              if status in [
                  StatusType.SUCCESS,
                  StatusType.DONOTADVANCE,
              ]:
                  cost = result
              else:
                  cost = self.worst_possible_result
  
          except Empty:
              info = None
              if obj.exit_status is pynisher.TimeoutException:
                  status = StatusType.TIMEOUT
                  additional_run_info = {"error": "Timeout"}
              elif obj.exit_status is pynisher.MemorylimitException:
                  status = StatusType.MEMOUT
                  additional_run_info = {
                      "error": "Memout (used more than {} MB).".format(
                          self.memory_limit
                      )
                  }
              else:
                  raise ValueError(obj.exit_status)
              cost = self.worst_possible_result
  
      elif obj.exit_status is TAEAbortException:
          info = None
          status = StatusType.ABORT
          cost = self.worst_possible_result
          additional_run_info = {
              "error": "Your configuration of "
              "auto-sklearn does not work!",
              "exit_status": _encode_exit_status(obj.exit_status),
              "subprocess_stdout": obj.stdout,
              "subprocess_stderr": obj.stderr,
          }
  
      else:
          try:
              info = autosklearn.evaluation.util.read_queue(queue)
              result = info[-1]["loss"]
              status = info[-1]["status"]
              additional_run_info = info[-1]["additional_run_info"]
  
              if obj.exit_status == 0:
                  cost = result
              else:
                  status = StatusType.CRASHED
                  cost = self.worst_possible_result
                  additional_run_info["info"] = (
                      "Run treated as crashed "
                      "because the pynisher exit "
                      "status %s is unknown." % str(obj.exit_status)
                  )
                  additional_run_info[
                      "exit_status"
                  ] = _encode_exit_status(obj.exit_status)
                  additional_run_info["subprocess_stdout"] = obj.stdout
                  additional_run_info["subprocess_stderr"] = obj.stderr
          except Empty:
              info = None
              additional_run_info = {
                  "error": "Result queue is empty",
                  "exit_status": _encode_exit_status(obj.exit_status),
                  "subprocess_stdout": obj.stdout,
                  "subprocess_stderr": obj.stderr,
                  "exitcode": obj.exitcode,
              }
              status = StatusType.CRASHED
              cost = self.worst_possible_result
  
      if (
          self.budget_type is None or budget == 0
      ) and status == StatusType.DONOTADVANCE:
          status = StatusType.SUCCESS
  
      if not isinstance(additional_run_info, dict):
          additional_run_info = {"message": additional_run_info}
  
      if (
          info is not None
          and self.resampling_strategy
          in ("holdout-iterative-fit", "cv-iterative-fit")
          and status != StatusType.CRASHED
      ):
          learning_curve = (
              autosklearn.evaluation.util.extract_learning_curve(info)
          )
          learning_curve_runtime = (
              autosklearn.evaluation.util.extract_learning_curve(
                  info, "duration"
              )
          )
          if len(learning_curve) > 1:
              additional_run_info["learning_curve"] = learning_curve
              additional_run_info[
                  "learning_curve_runtime"
              ] = learning_curve_runtime
  
          train_learning_curve = (
              autosklearn.evaluation.util.extract_learning_curve(
                  info, "train_loss"
              )
          )
          if len(train_learning_curve) > 1:
              additional_run_info[
                  "train_learning_curve"
              ] = train_learning_curve
              additional_run_info[
                  "learning_curve_runtime"
              ] = learning_curve_runtime
  
          if self._get_test_loss:
              test_learning_curve = (
                  autosklearn.evaluation.util.extract_learning_curve(
                      info,
                      "test_loss",
                  )
              )
              if len(test_learning_curve) > 1:
                  additional_run_info[
                      "test_learning_curve"
                  ] = test_learning_curve
                  additional_run_info[
                      "learning_curve_runtime"
                  ] = learning_curve_runtime
  
      if isinstance(config, int):
          origin = "DUMMY"
          config_id = config
      else:
          origin = getattr(config, "origin", "UNKNOWN")
          config_id = config.config_id
  
      additional_run_info["configuration_origin"] = origin
  
      runtime = float(obj.wall_clock_time)
  
      autosklearn.evaluation.util.empty_queue(queue)
      self.logger.info(
          "Finished evaluating configuration %d" % config_id
      )
  
      # Do some sanity checking (for multi objective)
      if len(self.multi_objectives) > 1:
          error = (
              f"Returned costs {cost} does not match the number of objectives"
              f" {len(self.multi_objectives)}."
          )
  
          # If dict convert to array
          # Make sure the ordering is correct
          if isinstance(cost, dict):
              ordered_cost = []
              for name in self.multi_objectives:
                  if name not in cost:
                      raise RuntimeError(
                          f"Objective {name} was not found "
                          f"in the returned costs ({cost})"
                      )
  
                  ordered_cost.append(cost[name])
              cost = ordered_cost
  
          if isinstance(cost, list):
              if len(cost) != len(self.multi_objectives):
                  raise RuntimeError(error)
  
          if isinstance(cost, float):
              raise RuntimeError(error)
  
      return status, cost, runtime, additional_run_info
  
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 3 for calling the ExecuteTaFuncWithQueue obj's underlying member method code with example usage: obj.run(config, instance, cutoff, seed, budget, instance_specific) and returns: status,  cost,  runtime,  additional_run_info, StatusType,  self,  int,  additional_run_info