code: def _label_encode_target(
      self, data: DataFrame, stage: str
  ) -> DataFrame:
      if self.config.task != "classification":
          return data
      if stage == "fit" or self.label_encoder is None:
          self.label_encoder = LabelEncoder()
          data[
              self.config.target[0]
          ] = self.label_encoder.fit_transform(
              data[self.config.target[0]]
          )
      else:
          if self.config.target[0] in data.columns:
              data[
                  self.config.target[0]
              ] = self.label_encoder.transform(
                  data[self.config.target[0]]
              )
      return data
  
imports: import re
  import warnings
  from enum import Enum
  from pathlib import Path
  from typing import Dict, Iterable, List, Optional, Tuple, Union
  import joblib
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from omegaconf import DictConfig
  from pandas import DataFrame, DatetimeTZDtype, to_datetime
  from pandas.tseries import offsets
  from pandas.tseries.frequencies import to_offset
  from sklearn.base import TransformerMixin, copy
  from sklearn.preprocessing import FunctionTransformer, LabelEncoder, PowerTransformer, QuantileTransformer, StandardScaler
  from torch.utils.data import DataLoader, Dataset
  from pytorch_tabular.config import InferredConfig
  from pytorch_tabular.utils import get_logger
  from .categorical_encoders import OrdinalEncoder
label_desc: Use image node_id 20 for calling the TabularDatamodule obj's underlying member method code with example usage: obj._label_encode_target(data, stage) and returns: data, data