code: def train(args, train_loader, gpmodule, optimizer, loss_fn, epoch):
      for batch_idx, (data, target) in enumerate(train_loader):
          if args.cuda:
              data, target = data.cuda(), target.cuda()
          if args.binary:
              target = (
                  target % 2
              ).float()  # convert numbers 0->9 to 0 or 1
  
          gpmodule.set_data(data, target)
          optimizer.zero_grad()
          loss = loss_fn(gpmodule.model, gpmodule.guide)
          loss.backward()
          optimizer.step()
          batch_idx = batch_idx + 1
          if batch_idx % args.log_interval == 0:
              print(
                  "Train Epoch: {:2d} [{:5d}/{} ({:2.0f}%)]\tLoss: {:.6f}".format(
                      epoch,
                      batch_idx * len(data),
                      len(train_loader.dataset),
                      100.0 * batch_idx / len(train_loader),
                      loss,
                  )
              )
  
file_path: pyro/examples/contrib/gp/sv-dkl.py
imports: import argparse
  import time
  import torch
  import torch.nn
  import torch.nn.functional
  from torchvision import transforms
  import pyro
  import pyro.contrib.gp
  import pyro.infer
  from pyro.contrib.examples.util import get_data_directory, get_data_loader
label_desc: Use image node_id 1 for calling a global function with example usage: train(args, train_loader, gpmodule, optimizer, loss_fn, epoch) without return types