code: def forward(self, x):
      out = F.relu(self.bn1(self.conv1(x)))
      out = F.relu(self.bn2(self.conv2(out)))
      out = self.bn3(self.conv3(out))
      x = self.shortcut(x)
      d = self.out_planes
      out = torch.cat(
          [
              x[:, :d, :, :] + out[:, :d, :, :],
              x[:, d:, :, :],
              out[:, d:, :, :],
          ],
          1,
      )
      out = F.relu(out)
      return out
  
file_path: nni/examples/trials/cifar10_pytorch/models/dpn.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