code: def forward(self, inputs, feat_list):
      pred_semantic, lr8x, [enc2x, enc4x] = self.lr_branch(feat_list)
      pred_detail, hr2x = self.hr_branch(
          inputs["img"], enc2x, enc4x, lr8x
      )
      pred_matte = self.f_branch(inputs["img"], lr8x, hr2x)
  
      if self.training:
          logit_dict = {
              "semantic": pred_semantic,
              "detail": pred_detail,
              "matte": pred_matte,
          }
          return logit_dict
      else:
          return pred_matte
  
imports: from collections import defaultdict
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import scipy
  import paddleseg
  from paddleseg.models import layers, losses
  from paddleseg import utils
  from paddleseg.cvlibs import manager, param_init
label_desc: Use image node_id 2 for calling the MODNetHead obj's underlying member method code with example usage: obj.forward(inputs, feat_list) and returns: logit_dict, pred_matte