class_docstr: shared set-up checks and methods across imputers
class_docstr_tok: ["shared","set-up","checks","and","methods","across","imputers"]
docstr: Replace missing data with the learned parameters.
  
  Parameters
  ----------
  X: pandas dataframe of shape = [n_samples, n_features]
      The data to be transformed.
  
  Returns
  -------
  X_new: pandas dataframe of shape = [n_samples, n_features]
      The dataframe without missing values in the selected variables.
docstr_tok: ["Replace","missing","data","with","the","learned","parameters",".","Parameters","--","--","--","--","--","X",":","pandas","dataframe","of","shape","=","[","n_samples",",","n_features","]","The","data","to","be","transformed",".","Returns","--","--","--","-","X_new",":","pandas","dataframe","of","shape","=","[","n_samples",",","n_features","]","The","dataframe","without","missing","values","in","the","selected","variables","."]
code: def transform(self, X: pd.DataFrame) -> pd.DataFrame:
      """
      Replace missing data with the learned parameters.
  
      Parameters
      ----------
      X: pandas dataframe of shape = [n_samples, n_features]
          The data to be transformed.
  
      Returns
      -------
      X_new: pandas dataframe of shape = [n_samples, n_features]
          The dataframe without missing values in the selected variables.
      """
  
      X = self._transform(X)
  
      # Replace missing data with learned parameters
      X.fillna(value=self.imputer_dict_, inplace=True)
  
      return X
  
file_path: feature_engine/feature_engine/imputation/base_imputer.py
imports: import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.validation import check_is_fitted
  from feature_engine.dataframe_checks import _check_X_matches_training_df, check_X
  from feature_engine._base_transformers.mixins import GetFeatureNamesOutMixin
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 2 for calling the BaseImputer obj's underlying member method code with example usage: obj.transform(X) and returns: X