[{'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 process_data(images, steering):\nshow_images = False\nx,y = [],[]\ncurrent_images = len(images)\nif (debug):\nprint("Recorded images", current_images)\nfor i in range(current_images):\nimage = mpimg.imread(images[i])\nif (show_images):\nprint(\'Image\',images[i],\'dimensions:\', image.shape,"steering",steering[i])\nimg = cv2.imread(images[i]) # reads BGR format\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # back to RGB format\nimg = cv2.cvtColor(img,cv2.COLOR_RGB2HSV)\nrandom_bright = .25+np.random.uniform()\nimg[:,:,2] = img[:,:,2]*random_bright\nimg = cv2.cvtColor(img,cv2.COLOR_HSV2RGB)\ncrop_img = img[40:130]\nheight, width = crop_img.shape[:2]\nnew_width = int(width/2)\nnew_height = int(height/2)\nresized_image = cv2.resize(crop_img,(new_width, new_height),fx=2, fy=2, interpolation = cv2.INTER_CUBIC)\nif (show_images):\nprint(\'Resized image dimesions:\', resized_image.shape)\nplt.imshow(resized_image)\nplt.show()\nflip_prob = np.random.random()\nif flip_prob > 0.5:\nflipped_image = cv2.flip(resized_image, 1)\nflipped_steering = steering[i]*(-1)\nimages[i] = flipped_image\nsteering[i] = flipped_steering\nif (show_images):\nprint(\'Flipped image dimensions:\', flipped_image.shape,\'Steering:\',flipped_steering)\nplt.imshow(flipped_image)\nplt.show()\nelse :\nimages[i] = resized_image\nif i > 40:\nshow_images = False\nif (debug):\nprint("Training images", len(images))\nx = np.array(images)\ny = np.vstack(steering)\nreturn x,y\nimages_processed,steering_processed = process_data(images_raw, steering_raw)\nif (debug):\nprint("\\nProcessed data: resized and trimmed")\nprint("images_processed length",len(images_processed),"shape",images_processed.shape)\nprint("steering_processed length",len(steering_processed),"shape",steering_processed.shape)'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]