code: def check_heterogeneous_rnn(gpu, dtype):
      communicator, rank_prev, rank_next = setup_communicator(gpu)
  
      with chainer.using_config("dtype", dtype):
          n, n_vocab, l = 100, 8, 10
          # Number of model parameters are different among processes.
          n_hid = (communicator.rank + 1) * 10
  
          X = [
              np.random.randint(
                  0,
                  n_vocab,
                  size=np.random.randint(l // 2, l + 1),
                  dtype=np.int32,
              )
              for _ in range(n)
          ]
          Y = (np.random.rand(n) * 2).astype(dtype)
          model = Model(
              n_vocab, n_hid, communicator, rank_next, rank_prev
          )
  
          if gpu:
              model.to_device(cupy.cuda.Device())
              X = [chainer.backends.cuda.to_gpu(x) for x in X]
              Y = chainer.backends.cuda.to_gpu(Y)
  
          for i in range(n):
              err = model(X[i : i + 1], Y[i : i + 1])
              err.backward()
  
          # Check if backprop finishes without deadlock.
          assert True
  
imports: import chainer
  import chainer.backends
  from chainer.backends.cuda import cupy
  import chainer.functions
  import chainer.links
  import chainer.testing
  import chainer.testing.attr
  import chainermn
  import numpy
  import pytest
label_desc: Use image node_id 5 for calling a global function with example usage: check_heterogeneous_rnn(gpu, dtype) without return types