class_docstr: @article{cao2020learning,
          author = {Cao, Yi-Jun and Lin, Chuan and Li, Yong-Jie},
          year = {2020},
          title = {Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss},
          journal = {IEEE Transactions on Multimedia},
          doi = {10.1109/TMM.2020.2987685}
  }
  
  dataset_name options: {'BSDS', 'NYUD'}
class_docstr_tok: ["@","article","{","cao2020learning",",","author","=","{","Cao",",","Yi-Jun","and","Lin",",","Chuan","and","Li",",","Yong-Jie","}",",","year","=","{","2020","}",",","title","=","{","Learning","Crisp","Boundaries","Using","Deep","Refinement","Network","and","Adaptive","Weighting","Loss","}",",","journal","=","{","IEEE","Transactions","on","Multimedia","}",",","doi","=","{","10.1109\/TMM.2020.2987685","}","}","dataset_name","options",":","{","'BSDS","'",",","'NYUD","'","}"]
code: def forward(self, x):
      end_points = self.encode(x)
      end_points = [
          end_points["feat1"],
          end_points["feat2"],
          end_points["feat3"],
          end_points["feat4"],
          end_points["feat5"],
      ]
      x = self.decode(*end_points)
      return x
  
imports: import torch.nn.functional
  from .utils import *
  from .backbone import *
label_desc: Use image node_id 2 for calling the DRNet obj's underlying member method code with example usage: obj.forward(x) and returns: x