code: def train(self):
      self.model.train()
      for batch_idx, (data, target) in enumerate(self.train_loader):
          data, target = data.to(self.device), target.to(self.device)
  
          self.optimizer.zero_grad()
          output = self.model(data)
          loss = F.cross_entropy(output, target)
          loss.backward()
          self.optimizer.step()
  
          # **✨ Increment the global step**
          tracker.add_global_step()
          # **✨ Store stats in the tracker**
          tracker.save({"loss.train": loss})
  
          #
          if batch_idx % self.train_log_interval == 0:
              # **✨ Save added stats**
              tracker.save()
  
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 1 for calling the Configs obj's underlying member method code with example usage: obj.train() without return types