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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 parse_individual(self, individual):
 """Converts a deap individual into a full list of parameters. Parameters individual: deap individual from optimization Details vary according to type of optimization, but parameters within deap individual are always between -1 and 1. This function converts them into the values used to actually build the model Returns ------- fullpars: list Full parameter list for model building. """

        scaled_ind = []
        for i in range(len(self.value_means)):
            scaled_ind.append(self.value_means[i] + (
                individual[i] * self.value_ranges[i]))
        fullpars = list(self.arrangement)
        for k in range(len(self.variable_parameters)):
            for j in range(len(fullpars)):
                if fullpars[j] == self.variable_parameters[k]:
                    fullpars[j] = scaled_ind[k]
        return fullpars