code: def forward(self, features, gt=None, masks=None, training=False):
      c2, c3, c4, c5 = features
      in5 = self.in5(c5)
      in4 = self.in4(c4)
      in3 = self.in3(c3)
      in2 = self.in2(c2)
  
      out4 = self.up5(in5) + in4  # 1/16
      out3 = self.up4(out4) + in3  # 1/8
      out2 = self.up3(out3) + in2  # 1/4
  
      p5 = self.out5(in5)
      p4 = self.out4(out4)
      p3 = self.out3(out3)
      p2 = self.out2(out2)
  
      fuse = torch.cat((p5, p4, p3, p2), 1)
      # this is the pred module, not binarization module;
      # We do not correct the name due to the trained model.
      binary = self.binarize(fuse)
      if self.training:
          result = OrderedDict(binary=binary)
      else:
          return binary
      if self.adaptive and self.training:
          if self.serial:
              fuse = torch.cat(
                  (
                      fuse,
                      nn.functional.interpolate(binary, fuse.shape[2:]),
                  ),
                  1,
              )
          thresh = self.thresh(fuse)
          thresh_binary = self.step_function(binary, thresh)
          result.update(thresh=thresh, thresh_binary=thresh_binary)
      return result
  
file_path: EasyOCR/easyocr/DBNet/decoders/seg_detector.py
imports: from collections import OrderedDict
  import torch
  import torch.nn
label_desc: Use image node_id 5 for calling the SegDetector obj's underlying member method code with example usage: obj.forward(features, gt, masks, training) and returns: result, binary