code: def transform_dataframe(
      self, dataframe: pd.DataFrame
  ) -> pd.DataFrame:
      train_df = super().transform_dataframe(dataframe)
      train_df = process_categorical_features(
          train_df, categorical_features
      )
      train_df = process_number_features(train_df, categorical_features)
  
      targets = (
          pd.read_csv(
              os.path.join(
                  self.raw_dataset_dir,
                  f"orange_small_train_{self.task_name}.labels",
              ),
              header=None,
          )[0]
          .astype(str)
          .apply(lambda x: "true" if x == "1" else "false")
      )
  
      train_idcs = pd.read_csv(
          os.path.join(
              self.raw_dataset_dir,
              f"stratified_train_idx_{self.task_name}.txt",
          ),
          header=None,
      )[0]
  
      val_idcs = pd.read_csv(
          os.path.join(
              self.raw_dataset_dir,
              f"stratified_test_idx_{self.task_name}.txt",
          ),
          header=None,
      )[0]
  
      processed_train_df = train_df.iloc[train_idcs].copy()
      processed_train_df["target"] = targets.iloc[train_idcs]
      processed_train_df["split"] = 0
  
      processed_val_df = train_df.iloc[val_idcs].copy()
      processed_val_df["target"] = targets.iloc[val_idcs]
      processed_val_df["split"] = 1
  
      if self.include_test_download:
          test_df = self.load_file_to_dataframe(
              os.path.join(
                  self.raw_dataset_dir, "orange_small_test.data"
              )
          )
          test_df[
              "target"
          ] = ""  # no ground truth labels for test download
          test_df["split"] = 2
          df = pd.concat(
              [processed_train_df, processed_val_df, test_df]
          )
      else:
          df = pd.concat([processed_train_df, processed_val_df])
  
      return df
  
imports: import os
  from typing import Optional
  import pandas
  from ludwig.datasets.dataset_config import DatasetConfig
  from ludwig.datasets.loaders.dataset_loader import DatasetLoader
label_desc: Use image node_id 3 for calling the KDDCup2009Loader obj's underlying member method code with example usage: obj.transform_dataframe(dataframe) and returns: df