code: def __init__(
      self,
      dataset: MetaDataset,
      model: nn.Module,
      metrics: MeterDict,
      log_dir: Path,
      batch_size: int,
      n_loader_workers: int = 0,
      cuda_device: int = 0,
      debug: bool = False,
      model_tag: str = "best",
  ):
      self.dataset = dataset
      self.metrics = metrics
      self.log_dir = log_dir
      self.batch_size = batch_size
      self.n_loader_workers = n_loader_workers
      self.cuda_device = cuda_device if pt.cuda.is_available() else -1
      self.pin_memory = self.cuda_device >= 0
      self.debug = debug
      if self.cuda_device >= 0:
          pt.cuda.set_device(self.cuda_device)
          self.model = model.cuda(self.cuda_device)
      else:
          self.model = model.cpu()
      self.load(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 1 to create a new Evaluator object from inherited base classes: object with example: obj = Evaluator(dataset, model, metrics, log_dir, batch_size, n_loader_workers, cuda_device, debug, model_tag)