[{'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:tokenizer.add_tokens([\'[A]\', \'[B]\', \'[P]\'])\ndef insert_tag(row):\n    to_be_inserted = sorted([\n        (row["A-offset"], " [A] "),\n        (row["B-offset"], " [B] "),\n        (row["Pronoun-offset"], " [P] ")\n    ], key=lambda x: x[0], reverse=True)\n    text = row["Text"]\n    for offset, tag in to_be_inserted:\n        text = text[:offset] + tag + text[offset:]\n    return text\n\ndef tokenize(text, tokenizer):\n    entries = {}\n    final_tokens = []\n    for token in tokenizer.tokenize(text):\n        if token in ("[A]", "[B]", "[P]"):\n            entries[token] = len(final_tokens)\n            continue\n        final_tokens.append(token)\n    return final_tokens, (entries["[A]"], entries["[B]"], entries["[P]"])\n\ndef target(row):\n    if int(row[\'A-coref\']) == 1:\n        return 0\n    elif int(row[\'B-coref\']) == 1:\n        return 1\n    else:\n        return 2\n"""\nThe lower part was taken from \n            [PyTorch] BERT + EndpointSpanExtractor + KFold\n"""\ndef children(m):\n    return m if isinstance(m, (list, tuple)) else list(m.children())\n\ndef set_trainable_attr(m, b):\n    m.trainable = b\n    for p in m.parameters():\n        p.requires_grad = b\n\ndef apply_leaf(m, f):\n    c = children(m)\n    if isinstance(m, nn.Module):\n        f(m)\n    if len(c) > 0:\n        for l in c:\n            apply_leaf(l, f)\n            \ndef set_trainable(l, b):\n    apply_leaf(l, lambda m: set_trainable_attr(m, b))\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]