code: def benchmark_step():
      global data_index
  
      if not args.disable_dynamic_topology:
          dynamic_topology_update(data_index)
      data = datasets[data_index % len(datasets)]
      data_index += 1
      optimizer.zero_grad()
      output = model(data)
      loss = F.cross_entropy(output, target)
      loss.backward()
      optimizer.step()
  
file_path: bluefog/examples/pytorch_benchmark.py
imports: import argparse
  import timeit
  import numpy
  import torch
  import torch.backends.cudnn
  from torch import nn
  import torch.nn.functional
  import torch.optim
  import torch.utils.data.distributed
  from torchvision import models
  import bluefog.torch
  from bluefog.common import topology_util
label_desc: Use image node_id 2 for calling a global function with example usage: benchmark_step() without return types