class_docstr: A custom dataset for loading and processing original TDC data, which is used as input data in DeepDTA model.
  
  Args:
       name (str): TDC dataset name.
       split (str): Data split type (train, valid or test).
       path (str): dataset download/local load path (default: "./data")
       mode (str): encoding mode (default: cnn_cnn)
       drug_transform: Transform operation (default: None)
       protein_transform: Transform operation (default: None)
       y_log (bool): Whether convert y values to log space. (default: True)
code: def __len__(self):
      return len(self.data)
  
file_path: pykale/kale/loaddata/tdc_datasets.py
imports: import torch
  from tdc.multi_pred import DTI
  from torch.utils import data
  from kale.prepdata.chem_transform import integer_label_protein, integer_label_smiles
label_desc: Use image node_id 2 for calling the BindingDBDataset obj's underlying member method code with example usage: obj.__len__() and returns: len