class_docstr: TrainLoop contains the core training loop logic.
class_docstr_tok: ["TrainLoop","contains","the","core","training","loop","logic","."]
code: def _run_single_eval(
      self,
      opt,
      valid_world,
      max_exs,
      datatype,
      is_multitask,
      task,
      index,
  ):
      # run evaluation on a single world
      valid_world.reset()
  
      world_logger = None
      task_opt = opt.copy()
      # set up world logger for the "test" fold
      if opt["world_logs"] and datatype == "test":
          task_opt["world_logs"] = get_task_world_logs(
              task, opt["world_logs"], is_multitask
          )
          world_logger = WorldLogger(task_opt)
  
      cnt = 0
      max_cnt = max_exs if max_exs > 0 else float("inf")
      while not valid_world.epoch_done() and cnt < max_cnt:
          valid_world.parley()
          if world_logger is not None:
              world_logger.log(valid_world)
          if cnt == 0 and opt["display_examples"]:
              print(valid_world.display() + "\n~~")
              print(valid_world.report())
              if opt["clearml_log"] and is_primary_worker():
                  self.clearml_logger.log_debug_samples(
                      datatype, valid_world.display(), index=index
                  )
          cnt = valid_world.report().get("exs") or 0
  
      if world_logger is not None:
          # dump world acts to file
          world_logger.reset()  # add final acts to logs
          if is_distributed():
              rank = get_rank()
              base_outfile, extension = os.path.splitext(
                  task_opt["world_logs"]
              )
              outfile = base_outfile + f"_{rank}" + extension
          else:
              outfile = task_opt["world_logs"]
          world_logger.write(
              outfile, valid_world, file_format=opt["save_format"]
          )
  
      valid_report = valid_world.report()
      if opt.get("validation_share_agent", False):
          valid_world.reset()  # make sure world doesn't remember valid data
  
      return valid_report
  
imports: import copy
  import random
  import torch
  import json
  import os
  import signal
  from typing import Tuple
  import numpy
  import parlai.utils.logging
  from parlai.core.agents import create_agent, create_agent_from_shared
  from parlai.core.exceptions import StopTrainException
  from parlai.core.logs import TensorboardLogger, WandbLogger, ClearMLLogger
  from parlai.core.metrics import Metric
  from parlai.core.metrics import aggregate_named_reports, aggregate_unnamed_reports, dict_report
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser, print_announcements
  from parlai.core.script import ParlaiScript, register_script
  from parlai.core.worlds import create_task, World
  from parlai.scripts.build_dict import build_dict, setup_args
  from parlai.scripts.eval_model import get_task_world_logs
  from parlai.utils.distributed import sync_object, is_primary_worker, all_gather_list, is_distributed, get_rank, num_workers
  from parlai.utils.io import PathManager
  from parlai.utils.misc import Timer, nice_report
  from parlai.utils.world_logging import WorldLogger
label_desc: Use image node_id 5 for calling the TrainLoop obj's underlying member method code with example usage: obj._run_single_eval(opt, valid_world, max_exs, datatype, is_multitask, task, index) and returns: valid_report