class_docstr: The GOOD-PCBA dataset. Adapted from `MoleculeNet
  <https://pubs.rsc.org/en/content/articlehtml/2018/sc/c7sc02664a>`_.
  
  Args:
      root (str): The dataset saving root.
      domain (str): The domain selection. Allowed: 'scaffold' and 'size'.
      shift (str): The distributional shift we pick. Allowed: 'no_shift', 'covariate', and 'concept'.
      subset (str): The split set. Allowed: 'train', 'id_val', 'id_test', 'val', and 'test'. When shift='no_shift',
          'id_val' and 'id_test' are not applicable.
      generate (bool): The flag for regenerating dataset. True: regenerate. False: download.
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code: def processed_file_names(self):
      return [
          "no_shift_train.pt",
          "no_shift_val.pt",
          "no_shift_test.pt",
          "covariate_train.pt",
          "covariate_val.pt",
          "covariate_test.pt",
          "covariate_id_val.pt",
          "covariate_id_test.pt",
          "concept_train.pt",
          "concept_val.pt",
          "concept_test.pt",
          "concept_id_val.pt",
          "concept_id_test.pt",
      ]
  
file_path: DIG/dig/oodgraph/good_pcba.py
imports: import os
  import os.path
  import gdown
  import torch
  from munch import Munch
  from torch_geometric.data import InMemoryDataset, extract_zip
label_desc: Use image node_id 6 for calling the GOODPCBA obj's underlying member method code with example usage: obj.processed_file_names() and returns: list