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):
              inputs = inputs.cuda(non_blocking=True)
              targets = targets.cuda(non_blocking=True)
  
              # 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
  
imports: import os
  import torch
  import torch.nn
  import torch.optim
  from keras import layers
  import keras
  import numpy
  import torch.multiprocessing
  import torch.distributed
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.data.distributed import DistributedSampler
label_desc: Use image node_id 3 for calling a global function with example usage: train(model, train_loader, num_epochs, optimizer, loss_fn) without return types