code: def _process_pdbs(
      self,
  ) -> Tuple[List[str], List[str], np.ndarray, List[str]]:
      if self.set_name == "general":
          data_folder = os.path.join(self.data_dir, "v2019-other-PL")
          index_labels_file = os.path.join(
              data_folder, "index/INDEX_general_PL_data.2019"
          )
      elif self.set_name == "refined":
          data_folder = os.path.join(self.data_dir, "refined-set")
          index_labels_file = os.path.join(
              data_folder, "index/INDEX_refined_data.2019"
          )
      elif self.set_name == "core":
          data_folder = os.path.join(self.data_dir, "v2013-core")
          index_labels_file = os.path.join(
              data_folder, "pdbbind_v2013_core.csv"
          )
  
      if self.set_name in ["general", "refined"]:
          # Extract locations of data
          with open(index_labels_file, "r") as g:
              pdbs = [
                  line[:4] for line in g.readlines() if line[0] != "#"
              ]
          # Extract labels
          with open(index_labels_file, "r") as g:
              labels = np.array(
                  [
                      # Lines have format
                      # PDB code, resolution, release year, -logKd/Ki, Kd/Ki, reference, ligand name
                      # The base-10 logarithm, -log kd/pk
                      float(line.split()[3])
                      for line in g.readlines()
                      if line[0] != "#"
                  ]
              )
      else:
          df = pd.read_csv(index_labels_file)
          pdbs = df.pdb_id.tolist()
          labels = np.array(df.label.tolist())
  
      if self.pocket:  # only load binding pocket
          protein_files = [
              os.path.join(data_folder, pdb, "%s_pocket.pdb" % pdb)
              for pdb in pdbs
          ]
      else:
          protein_files = [
              os.path.join(data_folder, pdb, "%s_protein.pdb" % pdb)
              for pdb in pdbs
          ]
      ligand_files = [
          os.path.join(data_folder, pdb, "%s_ligand.sdf" % pdb)
          for pdb in pdbs
      ]
  
      return (protein_files, ligand_files, labels, pdbs)
  
file_path: deepchem/deepchem/molnet/load_function/pdbbind_datasets.py
imports: import os
  import numpy
  import pandas
  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 3 for calling the _PDBBindLoader obj's underlying member method code with example usage: obj._process_pdbs() and returns: protein_files,  ligand_files,  labels,  pdbs