[{'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:sim_images = []\nsim_measurments = []\nfor line in lines[0:1000]:\nfor i in range(3):\nsource_path = line[i]\ntokens = source_path.split(\'/\')\nfilename = tokens[-1]\nlocal_path = "./data-udacity/IMG/"+filename\nimage = cv2.imread(local_path)\nsim_images.append(image)\ncorrecction = 0.2\nmeasurment = float(line[3])\nsim_measurments.append(measurment)\nsim_measurments.append(measurment + correcction)\nsim_measurments.append(measurment - correcction)\nimages =  []\nmeasurments =  []\nfor image, measurment in zip(sim_images, sim_measurments):\nfliped_image = cv2.flip(image,1)\nfliped_measurment =measurment*-1.0\nimages.append(image)\nimages.append(fliped_image)\nmeasurments.append(measurment)\nmeasurments.append(fliped_measurment)'}, {'role': 'assistant', 'content': "['load_data', 'data_preprocessing']"}]