code: def from_trainer(cls, trainer: Trainer, model_tag: str):
      return cls(
          dataset=trainer.dataset,
          model=trainer.get_model_core(),
          metrics=trainer.metrics,
          log_dir=trainer.log_dir,
          batch_size=trainer.eval_batch_size,
          n_loader_workers=trainer.n_loader_workers,
          cuda_device=trainer.cuda_device,
          debug=trainer.debug,
          model_tag=model_tag,
      )
  
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 8 for calling the Evaluator obj's underlying member method code with example usage: obj.from_trainer(cls, trainer, model_tag) and returns: cls