code: def forward_backward(network, data, label):
      # Ask autograd to remember the forward pass
      with autograd.record():
          # Compute the loss on all GPUs
          losses = [loss(network(X), Y) for X, Y in zip(data, label)]
  
      # Run the backward pass (calculate gradients) on all GPUs
      for l in losses:
          l.backward()
  
file_path: mxnet/example/distributed_training/cifar10_dist.py
imports: from __future__ import print_function
  import sys
  import random
  import numpy
  import mxnet
  from mxnet import autograd, gluon, kv, nd
  from mxnet.gluon.model_zoo import vision
label_desc: Use image node_id 3 for calling a global function with example usage: forward_backward(network, data, label) without return types