class_docstr: Shared methods across categorical transformers.
  
  - BaseEstimator brings methods get_params() and set_params().
  - TransformerMixin brings method fit_transform()
  - GetFeatureNamesOutMixin brings method get_feature_names_out().
class_docstr_tok: ["Shared","methods","across","categorical","transformers",".","-","BaseEstimator","brings","methods","get_params","(",")","and","set_params","(",")",".","-","TransformerMixin","brings","method","fit_transform","(",")","-","GetFeatureNamesOutMixin","brings","method","get_feature_names_out","(",")","."]
docstr: Convert the encoded variable back to the original values.
  
  Parameters
  ----------
  X: pandas dataframe of shape = [n_samples, n_features].
      The transformed dataframe.
  
  Returns
  -------
  X_tr: pandas dataframe of shape = [n_samples, n_features].
      The un-transformed dataframe, with the categorical variables containing the
      original values.
docstr_tok: ["Convert","the","encoded","variable","back","to","the","original","values",".","Parameters","--","--","--","--","--","X",":","pandas","dataframe","of","shape","=","[","n_samples",",","n_features","]",".","The","transformed","dataframe",".","Returns","--","--","--","-","X_tr",":","pandas","dataframe","of","shape","=","[","n_samples",",","n_features","]",".","The","un-transformed","dataframe",",","with","the","categorical","variables","containing","the","original","values","."]
code: def inverse_transform(self, X: pd.DataFrame) -> pd.DataFrame:
      """Convert the encoded variable back to the original values.
  
      Parameters
      ----------
      X: pandas dataframe of shape = [n_samples, n_features].
          The transformed dataframe.
  
      Returns
      -------
      X_tr: pandas dataframe of shape = [n_samples, n_features].
          The un-transformed dataframe, with the categorical variables containing the
          original values.
      """
  
      X = self._check_transform_input_and_state(X)
  
      # replace encoded categories by the original values
      for feature in self.encoder_dict_.keys():
          inv_map = {
              v: k for k, v in self.encoder_dict_[feature].items()
          }
          X[feature] = X[feature].map(inv_map)
  
      return X
  
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 7 for calling the CategoricalMethodsMixin obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: X