code: def test_load_from_pretrained(tmpdir, hf_tokenizer):
      # Create pretrained model
      from transformers.models.roberta import (
          RobertaConfig,
          RobertaForMaskedLM,
          RobertaForSequenceClassification,
      )
  
      config = RobertaConfig(vocab_size=hf_tokenizer.vocab_size)
      model = RobertaForMaskedLM(config)
      pretrained_model = HuggingFaceModel(
          model=model,
          tokenizer=hf_tokenizer,
          task="mlm",
          model_dir=tmpdir,
          device=torch.device("cpu"),
      )
      pretrained_model.save_checkpoint()
  
      # Create finetuning model
      config = RobertaConfig(
          vocab_size=hf_tokenizer.vocab_size,
          problem_type="regression",
          num_labels=1,
      )
      model = RobertaForSequenceClassification(config)
      finetune_model = HuggingFaceModel(
          model=model,
          tokenizer=hf_tokenizer,
          task="regression",
          model_dir=tmpdir,
          device=torch.device("cpu"),
      )
  
      # Load pretrained model
      finetune_model.load_from_pretrained()
  
      # check weights match
      pretrained_model_state_dict = pretrained_model.model.state_dict()
      finetune_model_state_dict = finetune_model.model.state_dict()
  
      pretrained_base_model_keys = [
          key
          for key in pretrained_model_state_dict.keys()
          if "roberta" in key
      ]
      matches = [
          torch.allclose(
              pretrained_model_state_dict[key],
              finetune_model_state_dict[key],
          )
          for key in pretrained_base_model_keys
      ]
  
      assert all(matches)
  
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 5 for calling a global function with example usage: test_load_from_pretrained(tmpdir, hf_tokenizer) without return types