code: def forward(
      self, cnn_feature, f_res, f_sub, train_mode=False, use_mlm=True
  ):
      if train_mode:
          if not use_mlm:
              g_output = self.pp(cnn_feature)  # b,25,512
              g_output = self.w_vrm(g_output)
              f_res = 0
              f_sub = 0
              return g_output, f_res, f_sub
          g_output = self.pp(cnn_feature)  # b,25,512
          f_res = self.pp_share(f_res)
          f_sub = self.pp_share(f_sub)
          g_output = self.w_vrm(g_output)
          f_res = self.w_share(f_res)
          f_sub = self.w_share(f_sub)
          return g_output, f_res, f_sub
      else:
          g_output = self.pp(cnn_feature)  # b,25,512
          g_output = self.w_vrm(g_output)
          return g_output
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal, XavierNormal
  import numpy
label_desc: Use image node_id 2 for calling the Prediction obj's underlying member method code with example usage: obj.forward(cnn_feature, f_res, f_sub, train_mode, use_mlm) and returns: g_output,  f_res,  f_sub, g_output, g_output,  f_res,  f_sub