class_docstr: Data configuration.
  
  Args:
      target (Optional[List[str]]): A list of strings with the names of the target column(s). It is
              mandatory for all except SSL tasks.
  
      continuous_cols (List): Column names of the numeric fields. Defaults to []
  
      categorical_cols (List): Column names of the categorical fields to treat differently. Defaults to
              []
  
      date_columns (List): (Column name, Freq, Format) tuples of the date fields. For eg. a field named
              introduction_date and with a monthly frequency like "2023-12" should have
              an entry ('intro_date','M','%Y-%m')
  
      encode_date_columns (bool): Whether to encode the derived variables from date
  
      validation_split (Optional[float]): Percentage of Training rows to keep aside as validation. Used
              only if Validation Data is not given separately
  
      continuous_feature_transform (Optional[str]): Whether to transform the features before
              modelling. By default, it is turned off. Choices are: [`None`,`yeo-johnson`,`box-cox`,
              `quantile_normal`,`quantile_uniform`].
  
      normalize_continuous_features (bool): Flag to normalize the input features(continuous)
  
      quantile_noise (int): NOT IMPLEMENTED. If specified fits QuantileTransformer on data with added
              gaussian noise with std = :quantile_noise: * data.std ; this will cause discrete values to be more
              separable. Please note that this transformation does NOT apply gaussian noise to the resulting
              data, the noise is only applied for QuantileTransformer
  
      num_workers (Optional[int]): The number of workers used for data loading. For windows always set to
              0
  
      pin_memory (bool): Whether to pin memory for data loading.
  
      handle_unknown_categories (bool): Whether to handle unknown or new values in categorical
              columns as unknown
  
      handle_missing_values (bool): Whether to handle missing values in categorical columns as
              unknown
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code: def __post_init__(self):
      assert (
          len(self.categorical_cols)
          + len(self.continuous_cols)
          + len(self.date_columns)
          > 0
      ), "There should be at-least one feature defined in categorical, continuous, or date columns"
      _validate_choices(self)
      if os.name == "nt" and self.num_workers != 0:
          print(
              "Windows does not support num_workers > 0. Setting num_workers to 0"
          )
          self.num_workers = 0
  
imports: import os
  import re
  from dataclasses import MISSING, dataclass, field
  from typing import Any, Dict, Iterable, List, Optional
  from omegaconf import OmegaConf
  from pytorch_tabular.models.common import heads
  from pytorch_tabular.utils import get_logger
label_desc: Use image node_id 1 for calling the DataConfig obj's underlying member method code with example usage: obj.__post_init__() without return types