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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_predict(self, y, exogenous=None, n_periods=10, **fit_args):
 """Fit an ARIMA to a vector, ``y``, of observations with an optional matrix of ``exogenous`` variables, and then generate predictions. Parameters y : array-like or iterable, shape=(n_samples,) The time-series to which to fit the ``ARIMA`` estimator. This may either be a Pandas ``Series`` object (statsmodels can internally use the dates in the index), or a numpy array. This should be a one-dimensional array of floats, and should not contain any ``np.nan`` or ``np.inf`` values. exogenous : array-like, shape=[n_obs, n_vars], optional (default=None) An optional 2-d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an ``ARIMA`` is fit on exogenous features, it must be provided exogenous features for making predictions. n_periods : int, optional (default=10) The number of periods in the future to forecast. fit_args : dict or kwargs, optional (default=None) Any keyword args to pass to the fit method. """

        self.fit(y, exogenous, **fit_args)
        return self.predict(n_periods=n_periods, exogenous=exogenous)