code: def text_clf_munge_data(params, local):
      train_data_path = f"{params.data_path}/{params.train_split}.csv"
      if params.valid_split is not None:
          valid_data_path = (
              f"{params.data_path}/{params.valid_split}.csv"
          )
      else:
          valid_data_path = None
      if os.path.exists(train_data_path):
          dset = AutoTrainDataset(
              train_data=[train_data_path],
              valid_data=[valid_data_path]
              if valid_data_path is not None
              else None,
              task="text_multi_class_classification",
              token=params.token,
              project_name=params.project_name,
              username=params.username,
              column_mapping={
                  "text": params.text_column,
                  "label": params.target_column,
              },
              percent_valid=None,  # TODO: add to UI
              local=local,
              convert_to_class_label=True,
          )
          params.data_path = dset.prepare()
          params.valid_split = "validation"
          params.text_column = "autotrain_text"
          params.target_column = "autotrain_label"
      return params
  
file_path: autotrain-advanced/src/autotrain/cli/utils.py
imports: import os
  from autotrain.dataset import AutoTrainDataset, AutoTrainDreamboothDataset
label_desc: Use image node_id 5 for calling a global function with example usage: text_clf_munge_data(params, local) and returns: params