code: def main():
      import torch.nn as nn
      import torch.distributed as dist
      import torch.optim as optim
      import torch.utils.data
  
      dist.init_process_group(backend="gloo")
      torch.manual_seed(42)
  
      data = torch.rand((1000, 32), dtype=torch.float32)
      labels = torch.randint(1, (1000, 10), dtype=torch.float32)
  
      train_dataset = torch.utils.data.TensorDataset(data, labels)
      train_sampler = torch.utils.data.distributed.DistributedSampler(
          train_dataset
      )
      train_loader = torch.utils.data.DataLoader(
          dataset=train_dataset,
          batch_size=32,
          shuffle=False,
          sampler=train_sampler,
      )
  
      model = nn.parallel.DistributedDataParallel(get_model())
      optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
      criterion = nn.BCELoss()
  
      for _ in range(2):
          # 2 epochs
          for _, (batch_data, batch_labels) in enumerate(train_loader):
              outputs = model(batch_data)
              loss = criterion(outputs.squeeze(), batch_labels)
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
  
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types