code: def test_output(self):
      n_layers = self.n_layers
      dropout_ratio = 0.0
      batch_size = 3
      input_size = 4
      hidden_size = 5
      seq_length = 6
  
      class Model(chainer.Chain):
          def __init__(self):
              super().__init__()
  
          def __call__(self, hx, ws1, ws2, ws3, bs, xs):
              ws = [F.separate(ws1) + F.separate(ws2)]
              if n_layers > 1:
                  ws.extend([F.separate(w) for w in F.separate(ws3)])
              bs = [F.separate(b) for b in F.separate(bs)]
              xs = F.separate(xs)
              hy, ys = F.n_step_gru(
                  n_layers, dropout_ratio, hx, ws, bs, xs
              )
              return hy, F.stack(ys, axis=0)
  
      model = Model()
  
      hx = input_generator.increasing(n_layers, batch_size, hidden_size)
      ws1 = input_generator.increasing(3, hidden_size, input_size)
      ws2 = input_generator.increasing(3, hidden_size, hidden_size)
      ws3 = input_generator.increasing(
          n_layers - 1, 6, hidden_size, hidden_size
      )
      bs = input_generator.increasing(n_layers, 6, hidden_size)
      xs = input_generator.increasing(
          seq_length, batch_size, input_size
      )
  
      self.expect(model, (hx, ws1, ws2, ws3, bs, xs))
  
file_path: chainer/tests/onnx_chainer_tests/functions_tests/test_rnn.py
imports: import chainer
  import chainer.functions
  import chainer.links
  from chainer import testing
  import numpy
  from onnx_chainer import onnx_helper
  from onnx_chainer.testing import input_generator
  from onnx_chainer_tests.helper import ONNXModelTest
label_desc: Use image node_id 1 for calling the TestNStepGRU obj's underlying member method code with example usage: obj.test_output() and returns: hy,  F