code: def train_one_epoch(model, data_loader, optimizer, epoch):
      model.train()
      for batch_idx, (data, target) in enumerate(data_loader):
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % log_interval == 0:
              print(
                  "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                      epoch,
                      batch_idx * len(data),
                      len(data_loader) * len(data),
                      100.0 * batch_idx / len(data_loader),
                      loss.item(),
                  )
              )
  
file_path: spark/python/test_support/test_pytorch_training_file.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  import tempfile
  import shutil
label_desc: Use image node_id 1 for calling a global function with example usage: train_one_epoch(model, data_loader, optimizer, epoch) without return types