code: def create_dataset(self) -> Dataset:
      dataset_file = os.path.join(
          self.data_dir, "BBBC001_v1_images_tif.zip"
      )
      labels_file = os.path.join(self.data_dir, "BBBC001_v1_counts.txt")
      if not os.path.exists(dataset_file):
          dc.utils.data_utils.download_url(
              url=BBBC1_IMAGE_URL, dest_dir=self.data_dir
          )
      if not os.path.exists(labels_file):
          dc.utils.data_utils.download_url(
              url=BBBC1_LABEL_URL, dest_dir=self.data_dir
          )
      labels_table = pd.read_csv(labels_file, delimiter="\t")
      labels = np.mean(
          [
              labels_table["manual count #1"],
              labels_table["manual count #2"],
          ],
          axis=0,
          dtype=int,
      )
  
      loader = dc.data.ImageLoader()
      return loader.create_dataset(
          inputs=(dataset_file, labels), in_memory=False
      )
  
file_path: deepchem/deepchem/molnet/load_function/bbbc_datasets.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
  import numpy
  import pandas
label_desc: Use image node_id 1 for calling the _BBBC001Loader obj's underlying member method code with example usage: obj.create_dataset() and returns: loader