code: def forward(self, x):
      if self.use_arcface:
          conv_feat = self.net.forward_features(x)
          # input = self.flatten(self.pool(conv_feat))
          xa = F.interpolate(
              x, [144, 144], mode="bilinear", align_corners=False
          )
          xa = xa[:, :, 8:120, 16:128]
          z = self.neta(xa)
          z = self.mlp(z)
  
          c = conv_feat
          for i in range(len(self.style_blocks)):
              c = self.style_blocks[i](c, z)
          feat3 = c
          feat2 = self.branch2d(conv_feat)
          conv_feat = torch.cat([feat3, feat2], dim=1)
          feat = self.flatten(self.pool(conv_feat))
          pred = self.fc(feat)
  
      elif self.no_gap:
          y = self.net.forward_features(x)
          pred = self.no_gap_output(y)
      else:
          pred = self.net(x)
          if self.use_eyes:
              eye_w1 = x.shape[3] // 8
              eye_w2 = x.shape[3] - wa
              hstep = x.shape[2] // 8
              eye_h1 = hstep * 2
              eye_h2 = hstep * 4
              x_eye = x[:, :, eye_h1:eye_h2, eye_w1:eye_w2]
              feate = self.nete(x_eye)
              # print(pred.shape, feate.shape)
              feat = torch.cat((pred, feate), 1)
              pred = self.fc(feat)
      return pred
  
imports: import os
  import time
  import timm
  import glob
  import numpy
  import os.path
  import torch
  import torch.distributed
  from torch import nn
  import torch.nn.functional
  from .iresnet import get_model
label_desc: Use image node_id 2 for calling the OneNetwork obj's underlying member method code with example usage: obj.forward(x) and returns: pred