[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:box_list = pickle.load(open("bbox_pickle.p", "rb"))\nimage = mpimg.imread(\'test_images/test3.jpg\')\nheat = np.zeros_like(image[:, :, 0]).astype(np.float)\nheat = add_heat(heat, box_list)\nheat = apply_threshold(heat, 1)  # shows far-left + far-right false positives\nheatmap = np.clip(heat, 0, 255)\nlabels = label(heatmap)\ndraw_img = draw_labeled_bboxes(np.copy(image), labels)\nfig = plt.figure()\nplt.subplot(121)\nplt.imshow(draw_img)\nplt.title(\'Car Positions Thresholded\')\nplt.subplot(122)\nplt.imshow(heatmap, cmap=\'hot\')\nplt.title(\'Heat Map Thresholded\')\nfig.tight_layout()'}, {'role': 'assistant', 'content': "['result_visualization']"}]