code: def validate(self):
      self.model.eval()
      valid_loss = 0
      correct = 0
      with torch.no_grad():
          for data, target in self.valid_loader:
              data, target = data.to(self.device), target.to(
                  self.device
              )
  
              output = self.model(data)
              valid_loss += F.cross_entropy(
                  output, target, reduction="sum"
              ).item()
              pred = output.argmax(dim=1, keepdim=True)
              correct += pred.eq(target.view_as(pred)).sum().item()
  
      valid_loss /= len(self.valid_loader.dataset)
      valid_accuracy = 100.0 * correct / len(self.valid_loader.dataset)
  
      # **Save stats**
      tracker.save(
          {"loss.valid": valid_loss, "accuracy.valid": valid_accuracy}
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torch.utils.data
  from torchvision import datasets, transforms
  from labml import experiment, lab, tracker, logger
  from labml.configs import BaseConfigs, option
label_desc: Use image node_id 2 for calling the Configs obj's underlying member method code with example usage: obj.validate() without return types