code: def test():
      model = SimpleNetChild()
      dataset = [
          (
              (numpy.ones((2, 5, 5)) * i).astype(numpy.float32),
              numpy.int32(0),
          )
          for i in range(100)
      ]
  
      batch_size = 5
      devices = tuple(
          [chainer.get_device(d) for d in sys.argv[1].split(",")]
      )
      iters = [
          chainer.iterators.SerialIterator(i, batch_size)
          for i in chainer.datasets.split_dataset_n_random(
              dataset, len(devices)
          )
      ]
      optimizer = chainer.optimizers.SGD(lr=1.0)
      optimizer.setup(model)
  
      # Initialize CUDA context.
      cuda.cupy.cuda.runtime.runtimeGetVersion()
  
      try:
          mpu.MultiprocessParallelUpdater(
              iters, optimizer, devices=devices
          )
      except RuntimeError as e:
          if sys.argv[2] == "fork":
              assert "CUDA context" in str(e)
              return
  
      updater = mpu.MultiprocessParallelUpdater(
          iters, optimizer, devices=devices
      )
      trainer = chainer.training.Trainer(updater, (1, "epoch"), "/tmp")
      trainer.run()
      assert sys.argv[2] != "fork"
  
file_path: chainer/tests/chainer_tests/training_tests/updaters_tests/snippets/cuda_init.py
imports: import multiprocessing
  import sys
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.training.updaters.multiprocess_parallel_updater
label_desc: Use image node_id 1 for calling a global function with example usage: test() without return types