code: def train_from_file(self, train_file, vocab_size, model_file, _):
      tokenizer = HuggingFaceBPETokenizer(
          HuggingFaceBPEModel(unk_token="[UNK]")
      )
      trainer = HuggingFaceBPETrainer(
          special_tokens=["[UNK]", "[PAD]"], vocab_size=vocab_size
      )
      tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()
  
      tokenizer.train([str(train_file)], trainer)
      tokenizer.save(model_file)
  
imports: import argparse
  import os
  from pathlib import Path
  from time import time
  from tabulate import tabulate
  from tokenizers import pre_tokenizers
  from tokenizers import Tokenizer
  from tokenizers.models import BPE
  from tokenizers.trainers import BpeTrainer
label_desc: Use image node_id 1 for calling the HuggingfaceInterface obj's underlying member method code with example usage: obj.train_from_file(train_file, vocab_size, model_file, _) without return types