code: def backward(ctx, *inputs):
      inputs = [i.data for i in inputs]
      inputs = [
          inputs[i : i + ctx.num_inputs]
          for i in range(0, len(inputs), ctx.num_inputs)
      ]
      results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0])
      outputs = comm.broadcast_coalesced(results, ctx.target_gpus)
      return (None,) + tuple(
          [Variable(t) for tensors in outputs for t in tensors]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import threading
  import torch
  import torch.cuda.comm
  from torch.autograd import Variable, Function
  from torch.nn.parallel.data_parallel import DataParallel
  from torch.nn.parallel.parallel_apply import get_a_var
  from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
label_desc: Use image node_id 2 for calling the AllReduce obj's underlying member method code with example usage: obj.backward(ctx) and returns: unknown