class_docstr: Shared set-up, checks and methods across creation transformers.
class_docstr_tok: ["Shared","set-up",",","checks","and","methods","across","creation","transformers","."]
docstr: This transformer does not learn parameters.
  
  Parameters
  ----------
  X: pandas dataframe of shape = [n_samples, n_features]
      The training input samples.
  
  y: pandas Series, or np.array. Defaults to None.
      It is not needed in this transformer. You can pass y or None.
docstr_tok: ["This","transformer","does","not","learn","parameters",".","Parameters","--","--","--","--","--","X",":","pandas","dataframe","of","shape","=","[","n_samples",",","n_features","]","The","training","input","samples",".","y",":","pandas","Series",",","or","np.array",".","Defaults","to","None",".","It","is","not","needed","in","this","transformer",".","You","can","pass","y","or","None","."]
code: def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
      """
      This transformer does not learn parameters.
  
      Parameters
      ----------
      X: pandas dataframe of shape = [n_samples, n_features]
          The training input samples.
  
      y: pandas Series, or np.array. Defaults to None.
          It is not needed in this transformer. You can pass y or None.
      """
  
      # check input dataframe
      X = check_X(X)
  
      # check variables are numerical
      self.variables_: List[
          Union[str, int]
      ] = _find_or_check_numerical_variables(X, self.variables)
  
      if hasattr(self, "reference"):
          _find_or_check_numerical_variables(X, self.reference)
  
      # check if dataset contains na
      if self.missing_values == "raise":
          _check_contains_na(X, self.variables_)
          _check_contains_inf(X, self.variables_)
          if hasattr(self, "reference"):
              _check_contains_na(X, self.reference)
              _check_contains_inf(X, self.reference)
  
      # save input features
      self.feature_names_in_ = X.columns.tolist()
  
      # save train set shape
      self.n_features_in_ = X.shape[1]
  
      return self
  
file_path: feature_engine/feature_engine/creation/base_creation.py
imports: from typing import List, Optional, 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._check_input_parameters.check_init_input_params import _check_param_drop_original, _check_param_missing_values
  from feature_engine._variable_handling.variable_type_selection import _find_or_check_numerical_variables
  from feature_engine.dataframe_checks import _check_contains_inf, _check_contains_na, _check_X_matches_training_df, check_X
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 2 for calling the BaseCreation obj's underlying member method code with example usage: obj.fit(X, y) and returns: self