[{'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:from skimage.transform import resize\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\n%matplotlib inline\n          \n\nORIG_SHAPE = (424,424)\nCROP_SIZE = (256,256)\nIMG_SHAPE = (64,64)\n\ndef get_image(path, x1,y1, shape, crop_size):\n    x = plt.imread(path)\n    x = x[x1:x1+crop_size[0], y1:y1+crop_size[1]]\n    x = resize(x, shape)\n    x = x/255.\n    return x\n    \ndef get_all_images(dataframe, shape=IMG_SHAPE, crop_size=CROP_SIZE):\n    x1 = (ORIG_SHAPE[0]-CROP_SIZE[0])//2\n    y1 = (ORIG_SHAPE[1]-CROP_SIZE[1])//2\n   \n    sel = dataframe.values\n    ids = sel[:,0].astype(int).astype(str)\n    y_batch = sel[:,1:]\n    x_batch = []\n    for i in tqdm(ids):\n        x = get_image('../input/44352/images_training_rev1/'+i+'.jpg', x1,y1, shape=shape, crop_size=crop_size)\n        x_batch.append(x)\n    x_batch = np.array(x_batch)\n    return x_batch, y_batch\n        \nX_train, y_train = get_all_images(df_train)\nX_test, y_test = get_all_images(df_test)"}, {'role': 'assistant', 'content': "['load_data']"}]