code: def predict(self, dataset: Optional[Dataset] = None) -> np.ndarray:
      if dataset is None:
          loader = self.test_loader
          data_size = self.dataset.test_size
      else:
          loader = MetaDataset._data_loader(
              dataset=dataset,
              batch_size=self.batch_size,
              shuffle=False,
              cuda_device=self.cuda_device,
              is_training=False,
              n_workers=self.n_loader_workers,
              n_batches=None,
          )
          data_size = len(dataset)
      device = (
          "cpu" if self.cuda_device < 0 else f"cuda:{self.cuda_device}"
      )
      prog_bar = tqdm(
          desc=f"({device}) test",
          total=int(math.ceil(data_size / self.batch_size)),
          unit="batch",
      )
      _ = self.model.eval()
      preds = []
      for batch, data in enumerate(loader):
          with pt.no_grad():
              result = self._predict(*data)
          preds.append(result)
          prog_bar.update(1)
          if self.debug and batch > 0:
              break
      prog_bar.close()
      preds = np.concatenate(preds, axis=0)
      return preds
  
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 6 for calling the Evaluator obj's underlying member method code with example usage: obj.predict(dataset) and returns: preds