code: def hf_tokenizer(tmpdir):
      filepath = os.path.join(tmpdir, "smiles.txt")
      with open(filepath, "w") as f:
          f.write(
              "CN(c1ccccc1)c1ccccc1C(=O)NCC1(O)CCOCC1\nCC[NH+](CC)C1CCC([NH2+]C2CC2)(C(=O)[O-])C1\n"
          )
          f.write(
              "COCC(CNC(=O)c1ccc2c(c1)NC(=O)C2)OC\nOCCn1cc(CNc2cccc3c2CCCC3)nn1\n"
          )
          f.write(
              "CCCCCCc1ccc(C#Cc2ccc(C#CC3=CC=C(CCC)CC3)c(C3CCCCC3)c2)c(F)c1\nO=C(NCc1ccc(F)cc1)N1CC=C(c2c[nH]c3ccccc23)CC1\n"
          )
      from tokenizers import ByteLevelBPETokenizer
      from transformers.models.roberta import RobertaTokenizerFast
  
      tokenizer = ByteLevelBPETokenizer()
      tokenizer.train(
          files=filepath,
          vocab_size=1_000,
          min_frequency=2,
          special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"],
      )
      tokenizer_path = os.path.join(tmpdir, "tokenizer")
      os.makedirs(tokenizer_path)
      tokenizer.save_model(tokenizer_path)
      tokenizer = RobertaTokenizerFast.from_pretrained(tokenizer_path)
      return tokenizer
  
file_path: deepchem/deepchem/models/torch_models/tests/test_hf_models.py
imports: import os
  import deepchem
  import numpy
  import pytest
label_desc: Use image node_id 1 for calling a global function with example usage: hf_tokenizer(tmpdir) and returns: tokenizer