[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:warp_zero = np.zeros_like(warped).astype(np.uint8)\ncolor_warp = np.dstack((warp_zero, warp_zero, warp_zero))\npts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])\npts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])\npts = np.hstack((pts_left, pts_right))\ncv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))\nplt.imshow(color_warp)\nnewwarp = cv2.warpPerspective(color_warp, np.linalg.inv(perspective_M), (img.shape[1], img.shape[0]))\nresult = cv2.addWeighted(dst_img, 1, newwarp, 0.3, 0)\nfont = cv2.FONT_HERSHEY_SIMPLEX\ncv2.putText(result,('Radius of curvature is %d m'%left_curverad),(50,50), font, 2,(255,255,255),3)\nplt.imshow(result)"}, {'role': 'assistant', 'content': 'result_visualization'}]