code: def main_fold(fold):
      import time
  
      import torch.nn as nn
      import torch.optim as optim
      import torch_xla.core.xla_model as xm
  
      from ignite.engine import Engine
  
      device = xm.xla_device(fold)
  
      comp_model = _XlaDistModel.create_from_context()
      assert comp_model.device() == device
  
      model = nn.Linear(100, 10)
  
      model.to(device)  # Move model before creating optimizer
      optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
  
      def training_step(engine, _):
          data = torch.rand(4, 100, device=device)
          model.train()
          data = data.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = output.sum()
          loss.backward()
          xm.optimizer_step(optimizer, barrier=True)
          return loss.item()
  
      trainer = Engine(training_step)
  
      # THIS CAN BE A CAUSE OF CRASH if DEVICE is OTHER THAN device
      tensor = torch.tensor([fold + 1.0], dtype=torch.float).to(
          comp_model.device()
      )
      xm.all_reduce("max", [tensor])
  
      time.sleep(0.01 * fold)
  
      trainer.run([0] * 100, max_epochs=2)
  
file_path: ignite/tests/ignite/distributed/comp_models/test_xla.py
imports: import os
  import pytest
  import torch
  from ignite.distributed.comp_models import has_xla_support
label_desc: Use image node_id 10 for calling a global function with example usage: main_fold(fold) and returns: loss