code: def prepare(self):
      train_df, valid_df = self.split()
      train_df, valid_df = self.prepare_columns(train_df, valid_df)
  
      train_df = Dataset.from_pandas(train_df)
      valid_df = Dataset.from_pandas(valid_df)
  
      if self.local:
          dataset = DatasetDict(
              {
                  "train": train_df,
                  "validation": valid_df,
              }
          )
          dataset.save_to_disk(f"{self.project_name}/autotrain-data")
      else:
          train_df.push_to_hub(
              f"{self.username}/autotrain-data-{self.project_name}",
              split="train",
              private=True,
              token=self.token,
          )
          valid_df.push_to_hub(
              f"{self.username}/autotrain-data-{self.project_name}",
              split="validation",
              private=True,
              token=self.token,
          )
      if self.local:
          return f"{self.project_name}/autotrain-data"
      return f"{self.username}/autotrain-data-{self.project_name}"
  
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 4 for calling the Seq2SeqPreprocessor obj's underlying member method code with example usage: obj.prepare() and returns: self, str, self, self, str