code: def freeze_token_type_embeddings(student, args):
      if args.student_type == "roberta":
          student.roberta.embeddings.token_type_embeddings.weight.requires_grad = (
              False
          )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/distillation/train.py
imports: import argparse
  import json
  import os
  import pickle
  import shutil
  import numpy
  import torch
  from distiller import Distiller
  from lm_seqs_dataset import LmSeqsDataset
  from transformers import BertConfig, BertForMaskedLM, BertTokenizer, DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer, GPT2Config, GPT2LMHeadModel, GPT2Tokenizer, RobertaConfig, RobertaForMaskedLM, RobertaTokenizer
  from utils import git_log, init_gpu_params, logger, set_seed
label_desc: Use image node_id 3 for calling a global function with example usage: freeze_token_type_embeddings(student, args) without return types