code: def flip_back(self, output_flipped, matched_parts):
      assert (
          output_flipped.ndim == 4
      ), "output_flipped should be [batch_size, num_joints, height, width]"
  
      output_flipped = output_flipped[:, :, :, ::-1]
  
      for pair in matched_parts:
          tmp = output_flipped[:, pair[0], :, :].copy()
          output_flipped[:, pair[0], :, :] = output_flipped[
              :, pair[1], :, :
          ]
          output_flipped[:, pair[1], :, :] = tmp
  
      return output_flipped
  
imports: from scipy.optimize import linear_sum_assignment
  from collections import abc, defaultdict
  import cv2
  import numpy
  import math
  import paddle
  import paddle.nn
  from keypoint_preprocess import get_affine_mat_kernel, get_affine_transform
label_desc: Use image node_id 2 for calling the HRNetPostProcess obj's underlying member method code with example usage: obj.flip_back(output_flipped, matched_parts) and returns: output_flipped