code: def pytorch_run_sgd(rank, size):
      """Distributed Synchronous SGD Example"""
      torch.manual_seed(1234)
      train_set, bsz = partition_dataset()
      model = Net()
  
      if torch.cuda.is_available():
          model.to("cuda")
  
      optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
  
      num_batches = math.ceil(len(train_set.dataset) / float(bsz))
      for epoch in range(10):
          epoch_loss = 0.0
          for data, target in train_set:
              optimizer.zero_grad()
              if torch.cuda.is_available():
                  data = data.to("cuda")
                  target = target.to("cuda")
              output = model(data)
              loss = F.nll_loss(output, target)
              epoch_loss += loss.item()
              loss.backward()
              average_gradients(model)
              optimizer.step()
  
          print(
              "Rank ",
              dist.get_rank(),
              ", epoch ",
              epoch,
              ": ",
              epoch_loss / num_batches,
          )
  
imports: import math
  import random
  import time
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchvision.datasets
  import torchvision.transforms
  import fiber
  from fiber.experimental import Ring
label_desc: Use image node_id 3 for calling a global function with example usage: pytorch_run_sgd(rank, size) without return types