parameters: (self)
return_statement: return self._transform
docstring: Apply transforms, and transform with the final estimator
  
          This also works where final estimator is ``None``: all prior
          transformations are applied.
  
          Parameters
          ----------
          X : iterable
              Data to transform. Must fulfill input requirements of first step
              of the pipeline.
  
          Returns
          -------
          Xt : array-like, shape = [n_samples, n_transformed_features]
docstring_summary: Apply transforms, and transform with the final estimator
docstring_tokens: Apply transforms and transform with the final estimator
function: def transform(self):
          """Apply transforms, and transform with the final estimator
  
          This also works where final estimator is ``None``: all prior
          transformations are applied.
  
          Parameters
          ----------
          X : iterable
              Data to transform. Must fulfill input requirements of first step
              of the pipeline.
  
          Returns
          -------
          Xt : array-like, shape = [n_samples, n_transformed_features]
          """
          # _final_estimator is None or has transform, otherwise attribute error
          if self._final_estimator is not None:
              self._final_estimator.transform
          return self._transform
function_tokens: def transform ( self ) : # _final_estimator is None or has transform, otherwise attribute error if self . _final_estimator is not None : self . _final_estimator . transform return self . _transform