code: def forward(self, x):
      h = F.relu(self.conv1_1(x), inplace=True)
      h = F.relu(self.conv1_2(h), inplace=True)
      h = F.max_pool2d(h, 2, 2)
  
      h = F.relu(self.conv2_1(h), inplace=True)
      h = F.relu(self.conv2_2(h), inplace=True)
      h = F.max_pool2d(h, 2, 2)
  
      h = F.relu(self.conv3_1(h), inplace=True)
      h = F.relu(self.conv3_2(h), inplace=True)
      h = F.relu(self.conv3_3(h), inplace=True)
      f3_3 = h
      h = F.max_pool2d(h, 2, 2)
  
      h = F.relu(self.conv4_1(h), inplace=True)
      h = F.relu(self.conv4_2(h), inplace=True)
      h = F.relu(self.conv4_3(h), inplace=True)
      f4_3 = h
      h = F.max_pool2d(h, 2, 2)
  
      h = F.relu(self.conv5_1(h), inplace=True)
      h = F.relu(self.conv5_2(h), inplace=True)
      h = F.relu(self.conv5_3(h), inplace=True)
      f5_3 = h
      h = F.max_pool2d(h, 2, 2)
  
      h = F.relu(self.fc6(h), inplace=True)
      h = F.relu(self.fc7(h), inplace=True)
      ffc7 = h
      h = F.relu(self.conv6_1(h), inplace=True)
      h = F.relu(self.conv6_2(h), inplace=True)
      f6_2 = h
      h = F.relu(self.conv7_1(h), inplace=True)
      h = F.relu(self.conv7_2(h), inplace=True)
      f7_2 = h
  
      f3_3 = self.conv3_3_norm(f3_3)
      f4_3 = self.conv4_3_norm(f4_3)
      f5_3 = self.conv5_3_norm(f5_3)
  
      cls1 = self.conv3_3_norm_mbox_conf(f3_3)
      reg1 = self.conv3_3_norm_mbox_loc(f3_3)
      cls2 = self.conv4_3_norm_mbox_conf(f4_3)
      reg2 = self.conv4_3_norm_mbox_loc(f4_3)
      cls3 = self.conv5_3_norm_mbox_conf(f5_3)
      reg3 = self.conv5_3_norm_mbox_loc(f5_3)
      cls4 = self.fc7_mbox_conf(ffc7)
      reg4 = self.fc7_mbox_loc(ffc7)
      cls5 = self.conv6_2_mbox_conf(f6_2)
      reg5 = self.conv6_2_mbox_loc(f6_2)
      cls6 = self.conv7_2_mbox_conf(f7_2)
      reg6 = self.conv7_2_mbox_loc(f7_2)
  
      # max-out background label
      chunk = torch.chunk(cls1, 4, 1)
      bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2])
      cls1 = torch.cat([bmax, chunk[3]], dim=1)
  
      return [
          cls1,
          reg1,
          cls2,
          reg2,
          cls3,
          reg3,
          cls4,
          reg4,
          cls5,
          reg5,
          cls6,
          reg6,
      ]
  
file_path: face-alignment/face_alignment/detection/sfd/net_s3fd.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the s3fd obj's underlying member method code with example usage: obj.forward(x) and returns: list