code: def benchmark_step():
      global 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: byteps/example/pytorch/elastic_benchmark_byteps.py
imports: from __future__ import print_function
  import argparse
  import torch.backends.cudnn
  import torch.nn.functional
  import torch.optim
  import torch.utils.data.distributed
  from torchvision import models
  import byteps.torch
  import timeit
  import numpy
  import os, sys
label_desc: Use image node_id 1 for calling a global function with example usage: benchmark_step() without return types