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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 transform(self, y, exogenous=None, n_periods=0, **_):
 """Create Fourier term features When an ARIMA is fit with an exogenous array, it must be forecasted with one also. Since at ``predict`` time in a pipeline we won't have ``y`` (and we may not yet have an ``exog`` array), we have to know how far into the future for which to compute Fourier terms (hence ``n_periods``). This method will compute the Fourier features for a given frequency and ``k`` term. Note that the ``y`` values are not used to compute these, so this does not pose a risk of data leakage. Parameters y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. This is unused and technically optional for the Fourier terms, since it uses the pre-computed ``n`` to calculate the seasonal Fourier terms. exogenous : array-like or None, shape=(n_samples, n_features), optional The exogenous array of additional covariates. If specified, the Fourier terms will be column-bound on the right side of the matrix. Otherwise, the Fourier terms will be returned as the new exogenous array. n_periods : int, optional (default=0) The number of periods in the future to forecast. If ``n_periods`` is 0, will compute the Fourier features for the training set. ``n_periods`` corresponds to the number of samples that will be returned. """

        check_is_fitted(self, "p_")
        _, exog = self._check_y_exog(y, exogenous, null_allowed=True)

        if n_periods and exog is not None:
            if n_periods != exog.shape[0]:
                raise ValueError("If n_periods and exog are specified, "
                                 "n_periods must match dims of exogenous")

        times = np.arange(self.n_ + n_periods, dtype=np.float64) + 1
        X_fourier = _fourier_terms(self.p_, times)

        # Maybe trim if we're in predict mode... in that case, we only keep the
        # last n_periods rows in the matrix we've created
        if n_periods:
            X_fourier = X_fourier[-n_periods:, :]

        if exog is None:
            exog = X_fourier
        else:
            exog = np.hstack([exog, X_fourier])

        return y, exog