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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 optimization_loop(self, timeSeries, forecastingMethod, remainingParameters, currentParameterValues=None):
 """The optimization loop. This function is called recursively, until all parameter values were evaluated. :param TimeSeries timeSeries: TimeSeries instance that requires an optimized forecast. :param BaseForecastingMethod forecastingMethod: ForecastingMethod that is used to optimize the parameters. :param list remainingParameters: List containing all parameters with their corresponding values that still need to be evaluated. When this list is empty, the most inner optimization loop is reached. :param dictionary currentParameterValues: The currently evaluated forecast parameter combination. :return: Returns a list containing a BaseErrorMeasure instance as defined in :py:meth:`BaseOptimizationMethod.__init__` and the forecastingMethods parameter. :rtype: list """


        if currentParameterValues is None:
            currentParameterValues = {}

        # The most inner loop is reached
        if 0 == len(remainingParameters):
            # set the forecasting parameters
            for parameter in currentParameterValues:
                forecastingMethod.set_parameter(parameter, currentParameterValues[parameter])

            # calculate the forecast
            forecast = timeSeries.apply(forecastingMethod)

            # create and initialize the ErrorMeasure
            error = self._errorClass(**self._errorMeasureKWArgs)

            # when the error could not be calculated, return an empty result
            if not error.initialize(timeSeries, forecast):
                return []

            # Debugging GridSearchTest.inner_optimization_result_test
            #print "Instance / SMAPE / Alpha: %s / %s / %s" % (
            #    str(error)[-12:-1],
            #    str(error.get_error(self._startingPercentage, self._endPercentage))[:8],
            #    currentParameterValues["smoothingFactor"]
            #)

            # return the result
            return [[error, dict(currentParameterValues)]]

        # If this is not the most inner loop than extract an additional parameter
        localParameter       = remainingParameters[-1]
        localParameterName   = localParameter[0]
        localParameterValues = localParameter[1]


        # initialize the result
        results = []

        # check the next level for each existing parameter
        for value in localParameterValues:
            currentParameterValues[localParameterName] = value
            remainingParameters = remainingParameters[:-1]
            results += self.optimization_loop(timeSeries, forecastingMethod, remainingParameters, currentParameterValues)

        return results