code: def _transform_continuous_columns(
      self, data: DataFrame, stage: str
  ) -> DataFrame:
      if stage == "fit":
          transform = self.CONTINUOUS_TRANSFORMS[
              self.config.continuous_feature_transform
          ]
          if (
              "random_state" in transform["params"]
              and self.seed is not None
          ):
              transform["params"]["random_state"] = self.seed
          self.continuous_transform = transform["callable"](
              **transform["params"]
          )
          # can be accessed through property continuous_transform
          data.loc[
              :, self.config.continuous_cols
          ] = self.continuous_transform.fit_transform(
              data.loc[:, self.config.continuous_cols]
          )
      else:
          data.loc[
              :, self.config.continuous_cols
          ] = self.continuous_transform.transform(
              data.loc[:, self.config.continuous_cols]
          )
      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 18 for calling the TabularDatamodule obj's underlying member method code with example usage: obj._transform_continuous_columns(data, stage) and returns: data