class_docstr: TrainLoop contains the core training loop logic.
class_docstr_tok: ["TrainLoop","contains","the","core","training","loop","logic","."]
code: def _run_final_extra_eval(self, opt):
      final_valid_opt = copy.deepcopy(opt)
      final_valid_opt_raw = Opt.load_init(opt["final_extra_opt"])
      final_datatype = final_valid_opt_raw["datatype"]
      for k, v in final_valid_opt_raw.items():
          final_valid_opt[k] = v
      final_max_exs = (
          final_valid_opt["validation_max_exs"]
          if final_valid_opt.get("short_final_eval")
          else -1
      )
      final_valid_world = load_eval_worlds(
          self.agent, final_valid_opt, final_datatype
      )
      final_valid_report = self._run_eval(
          final_valid_world,
          final_valid_opt,
          final_datatype,
          final_max_exs,
          write_log=True,
          extra_log_suffix="_extra",
      )
      if opt["wandb_log"] and is_primary_worker():
          self.wb_logger.log_final(final_datatype, final_valid_report)
      if opt["clearml_log"] and is_primary_worker():
          self.clearml_logger.log_final(
              final_datatype, final_valid_report
          )
  
      return final_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 7 for calling the TrainLoop obj's underlying member method code with example usage: obj._run_final_extra_eval(opt) and returns: final_valid_report