def METHOD_NAME(self, sample):
    n_pafs = len(BODY_PARTS_KPT_IDS)
    n_rows, n_cols, _ = sample['image'].shape
    paf_maps = np.zeros(shape=(n_pafs * 2, n_rows // self._stride, n_cols // self._stride), dtype=np.float32)
    label = sample['label']
    for paf_idx in range(n_pafs):
        keypoint_a = label['keypoints'][BODY_PARTS_KPT_IDS[paf_idx][0]]
        keypoint_b = label['keypoints'][BODY_PARTS_KPT_IDS[paf_idx][1]]
        if keypoint_a[2] <= 1 and keypoint_b[2] <= 1:
            self._set_paf(paf_maps[paf_idx * 2:paf_idx * 2 + 2],
                          keypoint_a[0], keypoint_a[1], keypoint_b[0], keypoint_b[1],
                          self._stride, self._paf_thickness)
        for another_annotation in label['processed_other_annotations']:
            keypoint_a = another_annotation['keypoints'][BODY_PARTS_KPT_IDS[paf_idx][0]]
            keypoint_b = another_annotation['keypoints'][BODY_PARTS_KPT_IDS[paf_idx][1]]
            if keypoint_a[2] <= 1 and keypoint_b[2] <= 1:
                self._set_paf(paf_maps[paf_idx * 2:paf_idx * 2 + 2],
                              keypoint_a[0], keypoint_a[1], keypoint_b[0], keypoint_b[1],
                              self._stride, self._paf_thickness)
    return paf_maps