code: def evaluate(self) -> None:
      device = (
          "cpu" if self.cuda_device < 0 else f"cuda:{self.cuda_device}"
      )
      prog_bar = tqdm(
          desc=f"({device}) test",
          total=int(
              math.ceil(self.dataset.test_size / self.batch_size)
          ),
          unit="batch",
      )
      _ = self.model.eval()
      for batch, data in enumerate(self.test_loader):
          with pt.no_grad():
              self._test(*data)
          prog_bar.update(1)
          if self.debug and batch > 0:
              break
      prog_bar.close()
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Optional, Iterable, Iterator
  from pathlib import Path
  import math
  from tqdm import tqdm
  from torch import Tensor
  from torch import nn
  from torch.utils.data import Dataset, DataLoader
  import torch
  import numpy
label_desc: Use image node_id 4 for calling the Evaluator obj's underlying member method code with example usage: obj.evaluate() without return types