[{'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:MAX_LEN = 32\n# Pad our input tokens\ninput_ids = pad_sequences([tokenizer.convert_tokens_to_ids(txt) for txt in tokenized_texts],\n                          maxlen=MAX_LEN, dtype="long", truncating="post", padding="post")\n# Use the BERT tokenizer to convert the tokens to their index numbers in the BERT vocabulary\ninput_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]\ninput_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype="long", truncating="post", padding="post")\n# Create attention masks\nattention_masks = []\n\n# Create a mask of 1s for each token followed by 0s for padding\nfor seq in input_ids:\n  seq_mask = [float(i>0) for i in seq]\n  attention_masks.append(seq_mask) '}, {'role': 'assistant', 'content': 'data_preprocessing'}]