class_docstr: Shared initialization parameters across transformers.
  
  Parameters
  ----------
  {variables}.
  
  {ignore_format}
class_docstr_tok: ["Shared","initialization","parameters","across","transformers",".","Parameters","--","--","--","--","--","{","variables","}",".","{","ignore_format","}"]
code: def __init__(
      self,
      variables: Union[None, int, str, List[Union[str, int]]] = None,
      ignore_format: bool = False,
  ) -> None:
      if not isinstance(ignore_format, bool):
          raise ValueError(
              "ignore_format takes only booleans True and False. "
              f"Got {ignore_format} instead."
          )
  
      self.variables = _check_init_parameter_variables(variables)
      self.ignore_format = ignore_format
  
imports: import warnings
  from typing import List, Union
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.validation import check_is_fitted
  from feature_engine._base_transformers.mixins import GetFeatureNamesOutMixin
  from feature_engine._docstrings.init_parameters import _ignore_format_docstring, _variables_categorical_docstring
  from feature_engine._docstrings.substitute import Substitution
  from feature_engine._variable_handling.init_parameter_checks import _check_init_parameter_variables
  from feature_engine._variable_handling.variable_type_selection import _find_all_variables, _find_or_check_categorical_variables
  from feature_engine.dataframe_checks import _check_contains_na, _check_X_matches_training_df, check_X
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 1 to create a new CategoricalInitMixin object  with example: obj = CategoricalInitMixin(variables, ignore_format)