[{'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:# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here\'s several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the "../input/" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nimport multiprocessing\nfor dirname, _, filenames in os.walk(\'/kaggle/input\'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n        \nmem_bytes = os.sysconf(\'SC_PAGE_SIZE\') * os.sysconf(\'SC_PHYS_PAGES\')  # e.g. 4015976448\nmem_gib = mem_bytes/(1024.**3)  # e.g. 3.74\nprint("RAM: %f GB" % mem_gib)\nprint("CORES: %d" % multiprocessing.cpu_count())\n\n# Any results you write to the current directory are saved as output.'}, {'role': 'assistant', 'content': "['helper_functions']"}]