How to use the datasets.ds_utils.validate_boxes function in datasets

self._class_to_ind[cls])
                                         for cls in self._classes[1:]])

        for ix, obj in enumerate(objs):
            cls = coco_cat_id_to_class_ind[obj['category_id']]
            boxes[ix, :] = obj['clean_bbox']
            gt_classes[ix] = cls
            seg_areas[ix] = obj['area']
            if obj['iscrowd']:
                # Set overlap to -1 for all classes for crowd objects
                # so they will be excluded during training
                overlaps[ix, :] = -1.0
            else:
                overlaps[ix, cls] = 1.0

        ds_utils.validate_boxes(boxes, width=width, height=height)
        overlaps = scipy.sparse.csr_matrix(overlaps)
        return {'boxes' : boxes,
                'gt_classes': gt_classes,
                'gt_overlaps' : overlaps,
                'flipped' : False,
                'seg_areas' : seg_areas}

self._class_to_ind[cls])
                                         for cls in self._classes[1:]])

        for ix, obj in enumerate(objs):
            cls = coco_cat_id_to_class_ind[obj['category_id']]
            boxes[ix, :] = obj['clean_bbox']
            gt_classes[ix] = cls
            seg_areas[ix] = obj['area']
            if obj['iscrowd']:
                # Set overlap to -1 for all classes for crowd objects
                # so they will be excluded during training
                overlaps[ix, :] = -1.0
            else:
                overlaps[ix, cls] = 1.0

        ds_utils.validate_boxes(boxes, width=width, height=height)
        overlaps = scipy.sparse.csr_matrix(overlaps)
        return {'width': width,
                'height': height,
                'boxes': boxes,
                'gt_classes': gt_classes,
                'gt_overlaps': overlaps,
                'flipped': False,
                'seg_areas': seg_areas}

self._class_to_ind[cls])
                                         for cls in self._classes[1:]])

        for ix, obj in enumerate(objs):
            cls = coco_cat_id_to_class_ind[obj['category_id']]
            boxes[ix, :] = obj['clean_bbox']
            gt_classes[ix] = cls
            seg_areas[ix] = obj['area']
            if obj['iscrowd']:
                # Set overlap to -1 for all classes for crowd objects
                # so they will be excluded during training
                overlaps[ix, :] = -1.0
            else:
                overlaps[ix, cls] = 1.0

        ds_utils.validate_boxes(boxes, width=width, height=height)
        overlaps = scipy.sparse.csr_matrix(overlaps)
        return {'boxes' : boxes,
                'gt_classes': gt_classes,
                'gt_overlaps' : overlaps,
                'flipped' : False,
                'seg_areas' : seg_areas}

self._class_to_ind[cls])
                                     for cls in self._classes[1:]])

    for ix, obj in enumerate(objs):
      cls = coco_cat_id_to_class_ind[obj['category_id']]
      boxes[ix, :] = obj['clean_bbox']
      gt_classes[ix] = cls
      seg_areas[ix] = obj['area']
      if obj['iscrowd']:
        # Set overlap to -1 for all classes for crowd objects
        # so they will be excluded during training
        overlaps[ix, :] = -1.0
      else:
        overlaps[ix, cls] = 1.0

    ds_utils.validate_boxes(boxes, width=width, height=height)
    overlaps = scipy.sparse.csr_matrix(overlaps)
    return {'width': width,
            'height': height,
            'boxes': boxes,
            'gt_classes': gt_classes,
            'gt_overlaps': overlaps,
            'flipped': False,
            'seg_areas': seg_areas}

for ix, obj in enumerate(objs):
      cls = self.coco_cat_id_to_class_ind[obj['category_id']]
      boxes[ix, :] = obj['clean_bbox']
      gt_classes[ix] = cls
      seg_areas[ix] = obj['area']
      if obj['iscrowd']:
        # Set overlap to -1 for all classes for crowd objects
        # so they will be excluded during training
        overlaps[ix, :] = -1.0
      else:
        overlaps[ix, cls] = 1.0

    ds_utils.validate_boxes(boxes, width=width, height=height)
    overlaps = scipy.sparse.csr_matrix(overlaps)
    return {'width': width,
            'height': height,
            'boxes': boxes,
            'gt_classes': gt_classes,
            'gt_overlaps': overlaps,
            'flipped': False,
            'seg_areas': seg_areas}

boxes = np.maximum(raw_data - 1, 0).astype(np.uint16)
            if method == 'MCG':
                # Boxes from the MCG website are in (y1, x1, y2, x2) order
                boxes = boxes[:, (1, 0, 3, 2)]
            # Remove duplicate boxes and very small boxes and then take top k
            keep = ds_utils.unique_boxes(boxes)
            boxes = boxes[keep, :]
            keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
            boxes = boxes[keep, :]
            boxes = boxes[:top_k, :]
            box_list.append(boxes)
            # Sanity check
            im_ann = self._COCO.loadImgs(index)[0]
            width = im_ann['width']
            height = im_ann['height']
            ds_utils.validate_boxes(boxes, width=width, height=height)
        return self.create_roidb_from_box_list(box_list, gt_roidb)

self._class_to_ind[cls])
                                     for cls in self._classes[1:]])

    for ix, obj in enumerate(objs):
      cls = coco_cat_id_to_class_ind[obj['category_id']]
      boxes[ix, :] = obj['clean_bbox']
      gt_classes[ix] = cls
      seg_areas[ix] = obj['area']
      if obj['iscrowd']:
        # Set overlap to -1 for all classes for crowd objects
        # so they will be excluded during training
        overlaps[ix, :] = -1.0
      else:
        overlaps[ix, cls] = 1.0

    ds_utils.validate_boxes(boxes, width=width, height=height)
    overlaps = scipy.sparse.csr_matrix(overlaps)
    return {'width': width,
            'height': height,
            'boxes': boxes,
            'gt_classes': gt_classes,
            'gt_overlaps': overlaps,
            'flipped': False,
            'seg_areas': seg_areas}

boxes = np.maximum(raw_data - 1, 0).astype(np.uint16)
            if method == 'MCG':
                # Boxes from the MCG website are in (y1, x1, y2, x2) order
                boxes = boxes[:, (1, 0, 3, 2)]
            # Remove duplicate boxes and very small boxes and then take top k
            keep = ds_utils.unique_boxes(boxes)
            boxes = boxes[keep, :]
            keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
            boxes = boxes[keep, :]
            boxes = boxes[:top_k, :]
            box_list.append(boxes)
            # Sanity check
            im_ann = self._COCO.loadImgs(index)[0]
            width = im_ann['width']
            height = im_ann['height']
            ds_utils.validate_boxes(boxes, width=width, height=height)
        return self.create_roidb_from_box_list(box_list, gt_roidb)