code: def flip_back(
      output_flipped, flip_pairs, target_type="GaussianHeatmap"
  ):
      """Flip the flipped heatmaps back to the original form.
      Note:
          - batch_size: N
          - num_keypoints: K
          - heatmap height: H
          - heatmap width: W
      Args:
          output_flipped (np.ndarray[N, K, H, W]): The output heatmaps obtained
              from the flipped images.
          flip_pairs (list[tuple()): Pairs of keypoints which are mirrored
              (for example, left ear -- right ear).
          target_type (str): GaussianHeatmap or CombinedTarget
      Returns:
          np.ndarray: heatmaps that flipped back to the original image
      """
      assert (
          len(output_flipped.shape) == 4
      ), "output_flipped should be [batch_size, num_keypoints, height, width]"
      shape_ori = output_flipped.shape
      channels = 1
      if target_type.lower() == "CombinedTarget".lower():
          channels = 3
          output_flipped[:, 1::3, ...] = -output_flipped[:, 1::3, ...]
      output_flipped = output_flipped.reshape(
          (shape_ori[0], -1, channels, shape_ori[2], shape_ori[3])
      )
      output_flipped_back = output_flipped.clone()
  
      # Swap left-right parts
      for left, right in flip_pairs:
          output_flipped_back[:, left, ...] = output_flipped[
              :, right, ...
          ]
          output_flipped_back[:, right, ...] = output_flipped[
              :, left, ...
          ]
      output_flipped_back = output_flipped_back.reshape(shape_ori)
      # Flip horizontally
      output_flipped_back = output_flipped_back[..., ::-1]
      return output_flipped_back
  
imports: import cv2
  import numpy
  import paddle.nn.functional
label_desc: Use image node_id 15 for calling a global function with example usage: flip_back(output_flipped, flip_pairs, target_type) and returns: output_flipped_back