code: def forward(ctx, num_inputs, *inputs):
      ctx.num_inputs = num_inputs
      ctx.target_gpus = [
          inputs[i].get_device()
          for i in range(0, len(inputs), num_inputs)
      ]
      inputs = [
          inputs[i : i + num_inputs]
          for i in range(0, len(inputs), num_inputs)
      ]
      # sort before reduce sum
      inputs = sorted(inputs, key=lambda i: i[0].get_device())
      results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0])
      outputs = comm.broadcast_coalesced(results, ctx.target_gpus)
      return tuple([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 1 for calling the AllReduce obj's underlying member method code with example usage: obj.forward(ctx, num_inputs) and returns: tuple