code: def valid_loader(self) -> Iterator:
      return self.dataset.valid_loader(
          batch_size=self.eval_batch_size,
          cuda_device=self.cuda_device,
          n_workers=self.n_loader_workers,
      )
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Optional, Union, Tuple, List, Iterator
  from pathlib import Path
  from functools import partial
  from copy import deepcopy
  import math
  import torch
  from torch import nn
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from .callback import Callback, CallbackList, Optimization, DebugStopper
  from .distributed import is_distributed, synchronize, is_main_process
label_desc: Use image node_id 7 for calling the Trainer obj's underlying member method code with example usage: obj.valid_loader() and returns: self