code: def copy(
      self,
      train: DataFrame,
      validation: DataFrame = None,
      target_transform: Optional[Union[TransformerMixin, Tuple]] = None,
      train_sampler: Optional[torch.utils.data.Sampler] = None,
      seed: Optional[int] = None,
      cache_data: str = None,
      copy_data: bool = None,
      verbose: bool = None,
      call_setup: bool = True,
      config_override: Optional[Dict] = {},
  ):
      if config_override is not None:
          for k, v in config_override.items():
              setattr(self.config, k, v)
      dm = TabularDatamodule(
          train=train,
          config=self.config,
          validation=validation,
          target_transform=target_transform or self.target_transforms,
          train_sampler=train_sampler or self.train_sampler,
          seed=seed or self.seed,
          cache_data=cache_data or self.cache_mode.value,
          copy_data=copy_data or True,
          verbose=verbose or self.verbose,
      )
      dm.categorical_encoder = self.categorical_encoder
      dm.continuous_transform = self.continuous_transform
      dm.scaler = self.scaler
      dm.label_encoder = self.label_encoder
      dm.target_transforms = self.target_transforms
      dm.setup(stage="ssl_finetune") if call_setup else None
      return dm
  
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 41 for calling the TabularDatamodule obj's underlying member method code with example usage: obj.copy(train, validation, target_transform, train_sampler, seed, cache_data, copy_data, verbose, call_setup, config_override) and returns: dm