[{'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:def multi_windows(img):\nrects = []\nystart = 400\nystop = 464\nscale = 1.0\nrects.append(find_cars(img, ystart=ystart, ystop=ystop, scale=scale, cspace=color_space,\nhog_channel=hog_channel, svc=svc, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block, show_all_rectangles=False))\nystart = 416\nystop = 480\nscale = 1.0\nrects.append(find_cars(img, ystart=ystart, ystop=ystop, scale=scale, cspace=color_space,\nhog_channel=hog_channel, svc=svc, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block, show_all_rectangles=False))\nystart = 400\nystop = 496\nscale = 1.5\nrects.append(find_cars(img, ystart=ystart, ystop=ystop, scale=scale, cspace=color_space,\nhog_channel=hog_channel, svc=svc, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block, show_all_rectangles=False))\nystart = 416\nystop = 536\nscale = 1.5\nrects.append(find_cars(img, ystart=ystart, ystop=ystop, scale=scale, cspace=color_space,\nhog_channel=hog_channel, svc=svc, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block, show_all_rectangles=False))\nystart = 440\nystop = 568\nscale = 2.0\nrects.append(find_cars(img, ystart=ystart, ystop=ystop, scale=scale, cspace=color_space,\nhog_channel=hog_channel, svc=svc, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block, show_all_rectangles=False))\nystart = 432\nystop = 560\nscale = 2.0\nrects.append(find_cars(img, ystart=ystart, ystop=ystop, scale=scale, cspace=color_space,\nhog_channel=hog_channel, svc=svc, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block, show_all_rectangles=False))\nystart = 472\nystop = 632\nscale = 2.5\nrects.append(find_cars(img, ystart=ystart, ystop=ystop, scale=scale, cspace=color_space,\nhog_channel=hog_channel, svc=svc, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block, show_all_rectangles=False))\nystart = 512\nystop = 672\nscale = 2.5\nrects.append(find_cars(img, ystart=ystart, ystop=ystop, scale=scale, cspace=color_space,\nhog_channel=hog_channel, svc=svc, orient=orient,\npix_per_cell=pix_per_cell, cell_per_block=cell_per_block, show_all_rectangles=False))\nrectangles = [item for sublist in rects for item in sublist]\nreturn rectangles\nrectangles = multi_windows(test_image)\ntest_img_rects = draw_boxes(test_image, rectangles, color=(0, 0, 255), thick=3)\nplt.figure(figsize=(10,10))\nplt.imshow(test_img_rects)\nprint('Number of boxes: ', len(rectangles))"}, {'role': 'assistant', 'content': 'result_visualization'}]