code: def iou_eval(self, dataset, train_step, buffer, model):
      test_config = DotDict(self.config.test[dataset])
  
      val_result_dir = os.path.join(
          self.config.results_dir,
          "{}/{}".format(dataset + "_iou", str(train_step)),
      )
  
      evaluator = DetectionIoUEvaluator()
  
      metrics = main_eval(
          None,
          self.config.train.backbone,
          test_config,
          evaluator,
          val_result_dir,
          buffer,
          model,
          self.mode,
      )
      if self.gpu == 0 and self.config.wandb_opt:
          wandb.log(
              {
                  "{} iou Recall".format(dataset): np.round(
                      metrics["recall"], 3
                  ),
                  "{} iou Precision".format(dataset): np.round(
                      metrics["precision"], 3
                  ),
                  "{} iou F1-score".format(dataset): np.round(
                      metrics["hmean"], 3
                  ),
              }
          )
  
imports: import argparse
  import os
  import shutil
  import time
  import multiprocessing
  import yaml
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import wandb
  from config.load_config import load_yaml, DotDict
  from data.dataset import SynthTextDataSet, CustomDataset
  from loss.mseloss import Maploss_v2, Maploss_v3
  from model.craft import CRAFT
  from eval import main_eval
  from metrics.eval_det_iou import DetectionIoUEvaluator
  from utils.util import copyStateDict, save_parser
label_desc: Use image node_id 7 for calling the Trainer obj's underlying member method code with example usage: obj.iou_eval(dataset, train_step, buffer, model) without return types