def __call__(self, results):
        """Performs the Flip augmentation.

        Args:
            results (dict): The resulting dict to be modified and passed
                to the next transform in pipeline.
        """
        _init_lazy_if_proper(results, self.lazy)
        if 'keypoint' in results:
            assert not self.lazy, ('Keypoint Augmentations are not compatible '
                                   'with lazy == True')
            assert self.direction == 'horizontal', (
                'Only horizontal flips are'
                'supported for human keypoints')

        modality = results['modality']
        if modality == 'Flow':
            assert self.direction == 'horizontal'

        flip = np.random.rand() < self.flip_ratio

        results['flip'] = flip
        results['flip_direction'] = self.direction
        img_width = results['img_shape'][1]

        if self.flip_label_map is not None and flip:
            results['label'] = self.flip_label_map.get(results['label'],
                                                       results['label'])

        if not self.lazy:
            if flip:
                if 'imgs' in results:
                    results['imgs'] = self._flip_imgs(results['imgs'],
                                                      modality)
                if 'keypoint' in results:
                    kp = results['keypoint']
                    kpscore = results.get('keypoint_score', None)
                    kp, kpscore = self._flip_kps(kp, kpscore, img_width)
                    results['keypoint'] = kp
                    if 'keypoint_score' in results:
                        results['keypoint_score'] = kpscore
        else:
            lazyop = results['lazy']
            if lazyop['flip']:
                raise NotImplementedError('Use one Flip please')
            lazyop['flip'] = flip
            lazyop['flip_direction'] = self.direction

        if 'gt_bboxes' in results and flip:
            assert not self.lazy and self.direction == 'horizontal'
            width = results['img_shape'][1]
            results['gt_bboxes'] = self._box_flip(results['gt_bboxes'], width)
            if 'proposals' in results and results['proposals'] is not None:
                assert results['proposals'].shape[1] == 4
                results['proposals'] = self._box_flip(results['proposals'],
                                                      width)

        return results
Performs the Flip augmentation.

        Args:
            results (dict): The resulting dict to be modified and passed
                to the next transform in pipeline.
        
EVA-01/video/datasets/pipeline.py