code: def test_trainer_allreduce_hybridsequential():
      contexts = [mx.cpu(0), mx.cpu(1)]
      net = mx.gluon.nn.HybridSequential()
      for _ in range(8):  # Create a network with 8 layers
          net.add(
              mx.gluon.nn.Dense(
                  1, weight_initializer="ones", bias_initializer="ones"
              )
          )
      net.initialize(ctx=contexts)
      net.hybridize()
      trainer = mx.gluon.Trainer(
          net.collect_params(), "sgd", update_on_kvstore=False
      )
      for ctx in contexts:
          with mx.autograd.record():
              out = net(mx.np.ones((1, 1), ctx=ctx))
          out.backward()
      trainer.allreduce_grads()
  
imports: import mxnet
  import unittest
  import os
  import numpy
  from mxnet import gluon
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_almost_equal
  from common import assertRaises, xfail_when_nonstandard_decimal_separator
  from copy import deepcopy
  import pytest
label_desc: Use image node_id 12 for calling a global function with example usage: test_trainer_allreduce_hybridsequential() without return types