[{'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 resize_image(image):\nshape = image.shape\nimage = image[math.floor(shape[0]/5):shape[0]-25, 0:shape[1]]\nimage1 = cv2.resize(image,(new_col, new_row),interpolation=cv2.INTER_AREA)\nreturn image1\ndef add_random_brightness(image, rand_val=.25+np.random.uniform()):\nimage_tran = cv2.cvtColor(image,cv2.COLOR_RGB2HSV)\nimage_tran[:,:,2] = image_tran[:,:,2]*rand_val\nreturn cv2.cvtColor(image_tran,cv2.COLOR_HSV2RGB)\ndef flip_image(image,y_steer):\nif np.random.randint(2) == 0:\nimage = cv2.flip(image,1)\ny_steer = -y_steer\nreturn image,y_steer\ndef preprocess_image(line):\ni = np.random.randint(3)\nif (i == 0):\nfile = line['center'][0].strip()\nshift_ang = 0.0\nif (i == 1):\nfile = line['left'][0].strip()\nshift_ang = 0.25\nif (i == 2):\nfile = line['right'][0].strip()\nshift_ang = -0.25\ny = line['steering'][0] + shift_ang\nimage = cv2.cvtColor(cv2.imread(file),cv2.COLOR_BGR2RGB)\nimage = add_random_brightness(image,.25+np.random.uniform())\nimage = np.array(resize_image(image))\nreturn flip_image(image,y)"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]