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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 update_and_transform(self, y, exogenous, **kwargs):
 """Update the params and return the transformed arrays Since no parameters really get updated in the Fourier featurizer, all we do is compose forecasts for ``n_periods=len(y)`` and then update ``n_``. Parameters y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. exogenous : array-like or None, shape=(n_samples, n_features) The exogenous array of additional covariates. **kwargs : keyword args Keyword arguments required by the transform function. """

        check_is_fitted(self, "p_")

        self._check_endog(y)
        _, Xt = self.transform(y, exogenous, n_periods=len(y), **kwargs)

        # Update this *after* getting the exog features
        self.n_ += len(y)
        return y, Xt