code: def forward(self, feat_list):
      enc2x, enc4x, enc32x = feat_list[0], feat_list[1], feat_list[4]
  
      enc32x = self.se_block(enc32x)
      lr16x = F.interpolate(
          enc32x, scale_factor=2, mode="bilinear", align_corners=False
      )
      lr16x = self.conv_lr16x(lr16x)
      lr8x = F.interpolate(
          lr16x, scale_factor=2, mode="bilinear", align_corners=False
      )
      lr8x = self.conv_lr8x(lr8x)
  
      pred_semantic = None
      if self.training:
          lr = self.conv_lr(lr8x)
          pred_semantic = F.sigmoid(lr)
  
      return pred_semantic, lr8x, [enc2x, enc4x]
  
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 LRBranch obj's underlying member method code with example usage: obj.forward(feat_list) and returns: pred_semantic,  lr8x,  list