class_docstr: Shared set-up procedures across numerical transformers, i.e.,
  variable transformers, discretisers, math combination.
class_docstr_tok: ["Shared","set-up","procedures","across","numerical","transformers",",","i.e.",",","variable","transformers",",","discretisers",",","math","combination","."]
docstr: Checks that the input is a dataframe and of the same size than the one used
  in the fit() method. Checks absence of NA and Inf.
  
  Parameters
  ----------
  X : Pandas DataFrame
  
  Raises
  ------
  TypeError
      If the input is not a Pandas DataFrame
  ValueError
      - If the variable(s) contain null values
      - If the df has different number of features than the df used in fit()
  
  Returns
  -------
  X : Pandas DataFrame.
      The same dataframe entered by the user.
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code: def transform(self, X: pd.DataFrame) -> pd.DataFrame:
      """
      Checks that the input is a dataframe and of the same size than the one used
      in the fit() method. Checks absence of NA and Inf.
  
      Parameters
      ----------
      X : Pandas DataFrame
  
      Raises
      ------
      TypeError
          If the input is not a Pandas DataFrame
      ValueError
          - If the variable(s) contain null values
          - If the df has different number of features than the df used in fit()
  
      Returns
      -------
      X : Pandas DataFrame.
          The same dataframe entered by the user.
      """
  
      # Check method fit has been called
      check_is_fitted(self)
  
      # check that input is a dataframe
      X = check_X(X)
  
      # Check if input data contains same number of columns as dataframe used to fit.
      _check_X_matches_training_df(X, self.n_features_in_)
  
      # check if dataset contains na or inf
      _check_contains_na(X, self.variables_)
      _check_contains_inf(X, self.variables_)
  
      # reorder variables to match train set
      X = X[self.feature_names_in_]
  
      return X
  
file_path: feature_engine/feature_engine/_base_transformers/base_numerical.py
imports: import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.validation import check_is_fitted
  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._base_transformers.mixins import GetFeatureNamesOutMixin
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 2 for calling the BaseNumericalTransformer obj's underlying member method code with example usage: obj.transform(X) and returns: X