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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 predict_is(self, h=5, fit_once=True, fit_method='MLE', intervals=False, **kwargs):
 """ Makes dynamic out-of-sample predictions with the estimated model on in-sample data Parameters h : int (default : 5) How many steps would you like to forecast? fit_once : boolean (default: True) Fits only once before the in-sample prediction; if False, fits after every new datapoint fit_method : string Which method to fit the model with intervals: boolean Whether to return prediction intervals Returns - pd.DataFrame with predicted values """
     
        predictions = []

        for t in range(0,h):
            x = NNAR(ar=self.ar, units=self.units, 
                layers=self.layers, data=self.data_original[:-h+t], family=self.family)
            if fit_once is False:
                x.fit(method=fit_method, printer=False)
            if t == 0:
                if fit_once is True:
                    x.fit(method=fit_method, printer=False)
                    saved_lvs = x.latent_variables
                predictions = x.predict(1, intervals=intervals)
            else:
                if fit_once is True:
                    x.latent_variables = saved_lvs
                predictions = pd.concat([predictions,x.predict(1, intervals=intervals)])
        
        if intervals is True:
            predictions.rename(columns={0:self.data_name, 1: "1% Prediction Interval", 
                2: "5% Prediction Interval", 3: "95% Prediction Interval", 4: "99% Prediction Interval"}, inplace=True)
        else:
            predictions.rename(columns={0:self.data_name}, inplace=True)

        predictions.index = self.index[-h:]

        return predictions