code: def train(model, device, train_loader, optimizer, epoch):
      model.train()
  
      for batch_idx, (data, target) in enumerate(train_loader):
          data, target = data.to(device), target.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % 10 == 0:
              model.history.log(
                  (epoch, batch_idx),
                  loss=loss,
                  conv1_weight=model.conv1.weight,
              )
  
              # At the end of each batch
              with model.canvas:
                  model.canvas.draw_plot(model.history["loss"])
                  model.canvas.draw_hist(model.history["conv1_weight"])
                  # TODO: c.draw_image(model.history["conv1_weight"])
  
              if batch_idx % 100 == 0:
                  model.canvas.save(
                      os.path.join(
                          OUTPUT_DIR, "pytorch_train_{}.png"
                      ).format(epoch)
                  )
              model.history.progress()
  
imports: import os
  import sys
  import shutil
  import unittest
  import torch
  from torch import nn
  import torch.nn.functional
  import torchvision.models
  from torchvision import datasets, transforms
  import matplotlib
  import hiddenlayer
label_desc: Use image node_id 1 for calling a global function with example usage: train(model, device, train_loader, optimizer, epoch) without return types