code: def test_epoch(self, data_loader):
      self.model.eval()
  
      scores = []
      losses = []
  
      for data in data_loader:
          feature = data[:, :, 0:-1].to(self.device)
          label = data[:, -1, -1].to(self.device)
  
          with torch.no_grad():
              pred = self.model(feature.float())  # .float()
              loss = self.loss_fn(pred, label)
              losses.append(loss.item())
  
              score = self.metric_fn(pred, label)
              scores.append(score.item())
  
      return np.mean(losses), np.mean(scores)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  import copy
  import math
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 7 for calling the TransformerModel obj's underlying member method code with example usage: obj.test_epoch(data_loader) and returns: np,  np