code: def prepare_columns(self, train_df, valid_df):
      drop_cols = [self.text_column]
      train_df.loc[:, "autotrain_text"] = train_df[self.text_column]
      valid_df.loc[:, "autotrain_text"] = valid_df[self.text_column]
      if self.prompt_column is not None:
          drop_cols.append(self.prompt_column)
          train_df.loc[:, "autotrain_prompt"] = train_df[
              self.prompt_column
          ]
          valid_df.loc[:, "autotrain_prompt"] = valid_df[
              self.prompt_column
          ]
      if self.rejected_text_column is not None:
          drop_cols.append(self.rejected_text_column)
          train_df.loc[:, "autotrain_rejected_text"] = train_df[
              self.rejected_text_column
          ]
          valid_df.loc[:, "autotrain_rejected_text"] = valid_df[
              self.rejected_text_column
          ]
  
      # drop drop_cols
      train_df = train_df.drop(columns=drop_cols)
      valid_df = valid_df.drop(columns=drop_cols)
      return train_df, valid_df
  
imports: from dataclasses import dataclass
  from typing import Optional
  import pandas
  from datasets import ClassLabel, Dataset, DatasetDict
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 3 for calling the LLMPreprocessor obj's underlying member method code with example usage: obj.prepare_columns(train_df, valid_df) and returns: train_df,  valid_df