[{'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:X = []\nY = []\nmasks_prefix = "/home/a.kondyukov/data/Indianapolis_masks_new/"\ncases = ["atherosclerosis", "calcinosis", "cardiomegaly_mild", "cardiomegaly_modsev", "normal", "tortuous_aorta"]\nfor i, case in enumerate(cases):\nfilenames = !ls /home/a.kondyukov/data/Indianapolis_masks_new/"$case""_masks" | grep "\\.png$"\nprint(case)\nprint("\\r", "0%", end="")\nmax_iter = len(filenames)\nfor j, filename in enumerate(filenames):\nprint("\\r", "%.2f" % (j / max_iter * 100) + "%", end="")\nref_img = plt.imread(os.path.join(masks_prefix, case + "_masks", filename))\ncur_img = imresize(ref_img, [IMAGE_SIZE, IMAGE_SIZE])[:, :, :3].mean(axis=2)\nX.append(cur_img)\nY.append(i)\nprint()\nX = np.array(X)\nY = np.array(Y)\nX /= X.max()'}, {'role': 'assistant', 'content': "['load_data']"}]