code: def train(model, train_loader, num_epochs, optimizer, loss_fn):
      for epoch in range(num_epochs):
          running_loss = 0.0
          for batch_idx, (inputs, targets) in enumerate(train_loader):
              # Forward pass
              outputs = model(inputs)
              loss = loss_fn(outputs, targets)
  
              # Backward and optimize
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
  
              running_loss += loss.item()
  
              # Print loss statistics
              if (batch_idx + 1) % 10 == 0:
                  print(
                      f"Epoch [{epoch+1}/{num_epochs}], "
                      f"Batch [{batch_idx+1}/{len(train_loader)}], "
                      f"Loss: {running_loss / 10}"
                  )
                  running_loss = 0.0
  
file_path: keras/examples/demo_custom_torch_workflow.py
imports: import os
  import torch
  import torch.nn
  import torch.optim
  from keras import layers
  import keras
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: train(model, train_loader, num_epochs, optimizer, loss_fn) without return types