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 __init__(
      self,
      name: str,
      split="train",
      path="./data",
      mode="cnn_cnn",
      y_log=True,
      drug_transform=None,
      protein_transform=None,
  ):
      self.data = DTI(name=name, path=path)
      self.mode = mode.lower()
      if y_log:
          self.data.convert_to_log()
      self.data = self.data.get_split()[split]
      self.drug_transform = drug_transform
      self.protein_transform = protein_transform
  
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 1 to create a new BindingDBDataset object from inherited base classes: data with example: obj = BindingDBDataset(name, split, path, mode, y_log, drug_transform, protein_transform)