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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fit_transform(self, X, y=None, **fit_params):
 """ Fit the model and transform with the final estimator Fits all the transforms one after the other and transforms the data, then uses fit_transform on transformed data with the final estimator. Parameters X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- Xt : array-like, shape = [n_samples, n_transformed_features] Transformed samples yt : array-like, shape = [n_samples] Transformed target """


        Xt, yt, fit_params = self._fit(X, y, **fit_params)

        if isinstance(self._final_estimator, XyTransformerMixin):
            Xt, yt, _ = self._final_estimator.fit_transform(Xt, yt)
        else:
            if hasattr(self._final_estimator, 'fit_transform'):
                Xt = self._final_estimator.fit_transform(Xt, yt)
            else:
                self._final_estimator.fit(Xt, yt)
                Xt = self._final_estimator.transform(Xt)

        self.N_fit = len(yt)

        return Xt, yt