def METHOD_NAME(self):
    # YOLOX multi-scale training, interpolate resize before inputs of the network.
    self._get_size()
    scale_y = self._input_size[0] / self.input_size[0]
    scale_x = self._input_size[1] / self.input_size[1]
    if scale_x != 1 or scale_y != 1:
        self.inputs['image'] = F.interpolate(
            self.inputs['image'],
            size=self._input_size,
            mode='bilinear',
            align_corners=False)
        gt_bboxes = self.inputs['gt_bbox']
        for i in range(len(gt_bboxes)):
            if len(gt_bboxes[i]) > 0:
                gt_bboxes[i][:, 0::2] = gt_bboxes[i][:, 0::2] * scale_x
                gt_bboxes[i][:, 1::2] = gt_bboxes[i][:, 1::2] * scale_y
        self.inputs['gt_bbox'] = gt_bboxes