code: def _set_target_transform(
      self, target_transform: Union[TransformerMixin, Tuple]
  ) -> None:
      if target_transform is not None:
          if isinstance(target_transform, Iterable):
              target_transform = FunctionTransformer(
                  func=target_transform[0],
                  inverse_func=target_transform[1],
              )
          self.do_target_transform = True
      else:
          self.do_target_transform = False
      self.target_transform_template = target_transform
  
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 13 for calling the TabularDatamodule obj's underlying member method code with example usage: obj._set_target_transform(target_transform) without return types