code: def test_channels_shuffle(self):
      class Net(nn.Module):
          def forward(self, x):
              bs, num_channels, height, width = x.size()
              x = x.reshape(bs * num_channels // 2, 2, height * width)
              x = x.permute(1, 0, 2)
              x = x.reshape(2, -1, num_channels // 2, height, width)
              return x[0], x[1]
  
      model = Net()
      x = torch.rand((1, 64, 224, 224), dtype=torch.float)
      self.run_test(model, (x,))
  
file_path: nni/test/algo/nas/graph_converter/test_convert_models.py
imports: import unittest
  import torch
  import nni.nas.nn.pytorch.layers
  from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
label_desc: Use image node_id 3 for calling the TestModels obj's underlying member method code with example usage: obj.test_channels_shuffle() and returns: x,  x