[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:import os\nfrom tqdm import tqdm\n\ndef test_image_generator(ids, shape=IMG_SHAPE):\n    x1 = (ORIG_SHAPE[0]-CROP_SIZE[0])//2\n    y1 = (ORIG_SHAPE[1]-CROP_SIZE[1])//2\n    x_batch = []\n    for i in ids:\n        x = get_image('../input/44352/images_test_rev1/'+i, x1, y1, shape=IMG_SHAPE, crop_size=CROP_SIZE)\n        x_batch.append(x)\n    x_batch = np.array(x_batch)\n    return x_batch\n\nval_files = os.listdir('../input/44352/images_test_rev1/')\nval_predictions = []\nN_val = len(val_files)\nfor i in tqdm(np.arange(0, N_val, batch_size)):\n    if i+batch_size > N_val:\n        upper = N_val\n    else:\n        upper = i+batch_size\n    X = test_image_generator(val_files[i:upper])\n    y_pred = model.predict(X)\n    val_predictions.append(y_pred)\nval_predictions = np.array(val_predictions)\nY_pred = np.vstack(val_predictions)\nids = np.array([v.split('.')[0] for v in val_files]).reshape(len(val_files),1)\nsubmission_df = pd.DataFrame(np.hstack((ids, Y_pred)), columns=df.columns)\nsubmission_df = submission_df.sort_values(by=['GalaxyID'])\nsubmission_df.to_csv('sample_submission.csv', index=False)"}, {'role': 'assistant', 'content': 'evaluation'}]