code: def _forward(self):
      if self.flip and not self.training and not self.deploy:
          self.inputs["image"] = paddle.concat(
              (
                  self.inputs["image"],
                  paddle.flip(self.inputs["image"], [3]),
              )
          )
      body_feats = self.backbone(self.inputs)
  
      if self.training:
          return self.hrhrnet_head(body_feats, self.inputs)
      else:
          outputs = self.hrhrnet_head(body_feats)
  
          if self.flip and not self.deploy:
              outputs = [paddle.split(o, 2) for o in outputs]
              output_rflip = [
                  paddle.flip(
                      paddle.gather(o[1], self.flip_perm, 1), [3]
                  )
                  for o in outputs
              ]
              output1 = [o[0] for o in outputs]
              heatmap = (output1[0] + output_rflip[0]) / 2.0
              tagmaps = [output1[1], output_rflip[1]]
              outputs = [heatmap] + tagmaps
          outputs = self.get_topk(outputs)
  
          if self.deploy:
              return outputs
  
          res_lst = []
          h = self.inputs["im_shape"][0, 0].numpy().item()
          w = self.inputs["im_shape"][0, 1].numpy().item()
          kpts, scores = self.post_process(*outputs, h, w)
          res_lst.append([kpts, scores])
          return res_lst
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from scipy.optimize import linear_sum_assignment
  from collections import abc, defaultdict
  import numpy
  import paddle
  from ppdet.core.workspace import register, create, serializable
  from .meta_arch import BaseArch
  from ..None import layers
  from ..keypoint_utils import transpred
label_desc: Use image node_id 3 for calling the HigherHRNet obj's underlying member method code with example usage: obj._forward() and returns: self, res_lst, outputs