code: def flip_back(output_flipped, matched_parts):
      assert (
          len(output_flipped.shape) == 4
      ), "output_flipped has to be [batch_size, num_joints, height, width]"
      output_flipped = torch.flip(output_flipped, dims=[3])
  
      for pair in matched_parts:
          tmp = output_flipped[:, pair[0]].clone()
          output_flipped[:, pair[0]] = output_flipped[:, pair[1]]
          output_flipped[:, pair[1]] = tmp
  
      return output_flipped
  
imports: import math
  import random
  from typing import Tuple
  import cv2
  import numpy
  import torch
  from torchvision.transforms import functional
  import matplotlib.pyplot
label_desc: Use image node_id 2 for calling a global function with example usage: flip_back(output_flipped, matched_parts) and returns: output_flipped