code: def forward(self, x):
      out = F.relu(self.bn1(self.conv1(x)))
      out = self.shuffle1(out)
      out = F.relu(self.bn2(self.conv2(out)))
      out = self.bn3(self.conv3(out))
      res = self.shortcut(x)
      out = (
          F.relu(torch.cat([out, res], 1))
          if self.stride == 2
          else F.relu(out + res)
      )
      return out
  
file_path: nni/examples/trials/cifar10_pytorch/models/shufflenet.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out