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__()
              with self.init_scope():
                  self.gru = L.NStepGRU(
                      n_layers, input_size, hidden_size, dropout_ratio
                  )
  
          def __call__(self, *xs):
              hy, ys = self.gru(None, xs)
              return [hy] + ys
  
      model = Model()
      xs = [
          input_generator.increasing(seq_length, input_size)
          for i in range(batch_size)
      ]
  
      # NOTE(msakai): Replace Permutate converter for avoiding error like:
      # ValidationError: Nodes in a graph must be topologically sorted, \
      # however input 'v330' of node:
      # input: "Permutate_0_const_empty" input: "v330" \
      # input: "Permutate_0_const_range" output: "Permutate_0_tmp_0" \
      # name: "Permutate_0_tmp_0" op_type: "Scatter"
      # is not output of any previous nodes.
      addon_converters = {
          "Permutate": convert_Permutate,
      }
      self.expect(
          model,
          xs,
          skip_opset_version=[7, 8],
          external_converters=addon_converters,
      )
  
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 TestNStepGRULink obj's underlying member method code with example usage: obj.test_output() and returns: unknown