code: def test_pretraining(hf_tokenizer, smiles_regression_dataset):
      from deepchem.models.torch_models.hf_models import (
          HuggingFaceModel,
      )
      from transformers.models.roberta import (
          RobertaConfig,
          RobertaForMaskedLM,
      )
  
      config = RobertaConfig(vocab_size=hf_tokenizer.vocab_size)
      model = RobertaForMaskedLM(config)
  
      hf_model = HuggingFaceModel(
          model=model,
          tokenizer=hf_tokenizer,
          task="mlm",
          device=torch.device("cpu"),
      )
      loss = hf_model.fit(smiles_regression_dataset, nb_epoch=1)
  
      assert loss
  
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 2 for calling a global function with example usage: test_pretraining(hf_tokenizer, smiles_regression_dataset) without return types