code: def _setup_cache(self, cache_data: Union[str, bool]) -> None:
      cache_data = cache_data.lower()
      if cache_data == self.CACHE_MODES.MEMORY.value:
          self.cache_mode = self.CACHE_MODES.MEMORY
      elif isinstance(cache_data, str):
          self.cache_mode = self.CACHE_MODES.DISK
          self.cache_dir = Path(cache_data)
          self.cache_dir.mkdir(parents=True, exist_ok=True)
      else:
          logger.warning(
              f"{cache_data} is not a valid path. Caching in memory"
          )
          self.cache_mode = self.CACHE_MODES.MEMORY
  
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 12 for calling the TabularDatamodule obj's underlying member method code with example usage: obj._setup_cache(cache_data) without return types