code: def test_time_sequence_prediction(self):
      class Sequence(nn.Module):  # torch.jit.ScriptModule
          def __init__(self):
              super(Sequence, self).__init__()
              self.lstm1 = nn.LSTMCell(1, 51)
              self.lstm2 = nn.LSTMCell(51, 51)
              self.linear = nn.Linear(51, 1)
  
          # @torch.jit.script_method
          def forward(self, input):
              # TODO: add future as input with default val
              # see https://github.com/pytorch/pytorch/issues/8724
              outputs = torch.empty((3, 0))
              h_t = torch.zeros((3, 51))
              c_t = torch.zeros((3, 51))
              h_t2 = torch.zeros((3, 51))
              c_t2 = torch.zeros((3, 51))
  
              output = torch.zeros([3, 51])
              future = 2
  
              # TODO: chunk call should appear as the for loop iterable
              # We hard-code it to 4 for now.
              a, b, c, d = input.chunk(input.size(1), dim=1)
              for input_t in (a, b, c, d):
                  h_t, c_t = self.lstm1(input_t, (h_t, c_t))
                  h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
                  output = self.linear(h_t2)
                  outputs = torch.cat((outputs, output), 1)
              for _ in range(future):  # if we should predict the future
                  h_t, c_t = self.lstm1(output, (h_t, c_t))
                  h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
                  output = self.linear(h_t2)
                  outputs = torch.cat((outputs, output), 1)
              return outputs
  
      class Traced(nn.Module):
          def __init__(self):
              super(Traced, self).__init__()
              self.seq = Sequence()
  
          def forward(self, input):
              return self.seq.forward(input)
  
      self.checkExportImport(Traced(), (torch.rand(3, 4),))
  
imports: import unittest
  import torch
  import torch.nn.functional
  import torchvision
  import nni.nas.nn.pytorch.layers
  from nni.nas.nn.pytorch import ParametrizedModule
  from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
label_desc: Use image node_id 7 for calling the TestConvert obj's underlying member method code with example usage: obj.test_time_sequence_prediction() and returns: outputs, self