docstr: Dataset to Load Tabular Data.
  
  Args:
      data (DataFrame): Pandas DataFrame to load during training
      task (str):
          Whether it is a classification or regression task. If classification, it returns a LongTensor as target
      continuous_cols (List[str], optional): A list of names of continuous columns. Defaults to None.
      categorical_cols (List[str], optional): A list of names of categorical columns.
          These columns must be ordinal encoded beforehand. Defaults to None.
      target (List[str], optional): A list of strings with target column name(s). Defaults to None.
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code: def __init__(
      self,
      data: DataFrame,
      task: str,
      continuous_cols: List[str] = None,
      categorical_cols: List[str] = None,
      target: List[str] = None,
  ):
      """Dataset to Load Tabular Data.
  
      Args:
          data (DataFrame): Pandas DataFrame to load during training
          task (str):
              Whether it is a classification or regression task. If classification, it returns a LongTensor as target
          continuous_cols (List[str], optional): A list of names of continuous columns. Defaults to None.
          categorical_cols (List[str], optional): A list of names of categorical columns.
              These columns must be ordinal encoded beforehand. Defaults to None.
          target (List[str], optional): A list of strings with target column name(s). Defaults to None.
      """
  
      self.task = task
      self.n = data.shape[0]
      self.target = target
      if target:
          self.y = data[target].astype(np.float32).values
          if isinstance(target, str):
              self.y = self.y.reshape(-1, 1)  # .astype(np.int64)
      else:
          self.y = np.zeros((self.n, 1))  # .astype(np.int64)
  
      if task == "classification":
          self.y = self.y.astype(np.int64)
      self.categorical_cols = (
          categorical_cols if categorical_cols else []
      )
      self.continuous_cols = continuous_cols if continuous_cols else []
  
      if self.continuous_cols:
          self.continuous_X = (
              data[self.continuous_cols].astype(np.float32).values
          )
  
      if self.categorical_cols:
          self.categorical_X = data[categorical_cols]
          self.categorical_X = self.categorical_X.astype(
              np.int64
          ).values
  
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 1 to create a new TabularDataset object from inherited base classes: Dataset with example: obj = TabularDataset(data, task, continuous_cols, categorical_cols, target)