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

        Args:
            results (dict): The resulting dict to be modified and passed
                to the next transform in pipeline.
        """
        _init_lazy_if_proper(results, False)

        if 'gt_bboxes' in results or 'proposals' in results:
            warnings.warn('TenCrop cannot process bounding boxes')

        imgs = results['imgs']

        img_h, img_w = results['imgs'][0].shape[:2]
        crop_w, crop_h = self.crop_size

        w_step = (img_w - crop_w) // 4
        h_step = (img_h - crop_h) // 4

        offsets = [
            (0, 0),                     # upper left
            (4 * w_step, 0),            # upper right
            (0, 4 * h_step),            # lower left
            (4 * w_step, 4 * h_step),   # lower right
            (2 * w_step, 2 * h_step),   # center
        ]

        img_crops = list()
        crop_bboxes = list()
        for x_offset, y_offsets in offsets:
            crop = [
                img[y_offsets:y_offsets + crop_h, x_offset:x_offset + crop_w]
                for img in imgs
            ]
            # import pdb
            # pdb.set_trace()
            # flip_crop = [np.flip(c, axis=1).copy() for c in crop]
            bbox = [x_offset, y_offsets, x_offset + crop_w, y_offsets + crop_h]
            img_crops.extend(crop)
            # img_crops.extend(flip_crop)
            crop_bboxes.extend([bbox for _ in range(len(imgs))])

        crop_bboxes = np.array(crop_bboxes)

        results['imgs'] = img_crops
        results['crop_bbox'] = crop_bboxes
        results['img_shape'] = results['imgs'][0].shape[:2]

        return results
Performs the TenCrop augmentation.

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