code: def create_dataset(self) -> Dataset:
      dataset_file = os.path.join(self.data_dir, "expt_gap.json")
      targz_file = os.path.join(self.data_dir, "expt_gap.tar.gz")
      if not os.path.exists(dataset_file):
          if not os.path.exists(targz_file):
              dc.utils.data_utils.download_url(
                  url=BANDGAP_URL, dest_dir=self.data_dir
              )
          dc.utils.data_utils.untargz_file(targz_file, self.data_dir)
      loader = dc.data.JsonLoader(
          tasks=self.tasks,
          feature_field="composition",
          label_field="experimental_bandgap",
          featurizer=self.featurizer,
      )
      return loader.create_dataset(dataset_file)
  
file_path: deepchem/deepchem/molnet/load_function/material_datasets/load_bandgap.py
imports: import os
  import deepchem
  from deepchem.molnet.load_function.molnet_loader import TransformerGenerator, _MolnetLoader
  from deepchem.data import Dataset
  from typing import List, Optional, Tuple, Union
label_desc: Use image node_id 1 for calling the _BandgapLoader obj's underlying member method code with example usage: obj.create_dataset() and returns: loader