(self)
return self._resample
Apply transformers/transformer_resamplers, and transform with the
        final estimator.

        This also works where final estimator is ``None``: all prior
        transformations are applied.

        Parameters
        ----------
        y : array-like, shape = (n_samples,)
            Data to resample. Must fulfill input requirements of first step
            of the pipeline.

        Returns
        -------
        yr : array-like, shape = (n_samples_new,)
Apply transformers/transformer_resamplers, and transform with the
        final estimator.
Apply transformers / transformer_resamplers and transform with the final estimator .
def resample(self):
        """Apply transformers/transformer_resamplers, and transform with the
        final estimator.

        This also works where final estimator is ``None``: all prior
        transformations are applied.

        Parameters
        ----------
        y : array-like, shape = (n_samples,)
            Data to resample. Must fulfill input requirements of first step
            of the pipeline.

        Returns
        -------
        yr : array-like, shape = (n_samples_new,)
        """
        # _final_estimator is None or has transform, otherwise attribute error
        if self._final_estimator != 'passthrough':
            self._final_estimator.resample
        return self._resample
def resample ( self ) : # _final_estimator is None or has transform, otherwise attribute error if self . _final_estimator != 'passthrough' : self . _final_estimator . resample return self . _resample