[{'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:def im_stats(im_stats_df):\nim_stats_d = {}\np = Pool(cpu_count())\nret = p.map(im_multi, im_stats_df['path'])\nfor i in range(len(ret)):\nim_stats_d[ret[i][0]] = ret[i][1]\nim_stats_df['size'] = im_stats_df['path'].map(lambda x: ' '.join(str(s) for s in im_stats_d[x]['size']))\nreturn im_stats_df\ndef get_im_cv2(path):\nimg = cv2.imread(path)\nreturn [path, img]\ndef normalize_image_features(paths):\nimf_d = {}\np = Pool(cpu_count())\nret = p.map(get_im_cv2, paths)\nfor i in range(len(ret)):\nimf_d[ret[i][0]] = ret[i][1]\nret = []\nfdata = [imf_d[f] for f in paths]\nfdata = np.array(fdata, dtype=np.uint8)\nfdata = fdata.transpose((0, 3, 1, 2))\nfdata = fdata.astype('float32')\nfdata = fdata / 255\nreturn fdata\nprint('Start pre-processing training set')\ntrain = glob.glob('./all_images_300/train/**/*.jpg') + glob.glob('./all_images_300/additional/**/*.jpg')\ntrain = pd.DataFrame([[p.split('/')[3],p.split('/')[4],p] for p in train], columns = ['type','image','path']) #limit for Kaggle Demo\ntrain = im_stats(train)\ntrain_data = normalize_image_features(train['path'])\nle = LabelEncoder()\ntrain_target = le.fit_transform(train['type'].values)\nprint(le.classes_) #in case not 1 to 3 order\nprint('Start pre-processing test set')\ntest = glob.glob('./all_images_300/test/*.jpg') + glob.glob('./all_images_300/test_stg2/*.jpg')\ntest = pd.DataFrame([[p.split('/')[3],p] for p in test], columns = ['image','path']) #[::20] #limit for Kaggle Demo\ntest_data = normalize_image_features(test['path'])\ntest_id = test.image.values"}, {'role': 'assistant', 'content': "['load_data', 'data_exploration']"}]