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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 parameters(self, sequence, value_means, value_ranges, arrangement):
 """Relates the individual to be evolved to the full parameter string. Parameters sequence: str Full amino acid sequence for specification object to be optimized. Must be equal to the number of residues in the model. value_means: list List containing mean values for parameters to be optimized. value_ranges: list List containing ranges for parameters to be optimized. Values must be positive. arrangement: list Full list of fixed and variable parameters for model building. Fixed values are the appropriate value. Values to be varied should be listed as 'var0', 'var1' etc, and must be in ascending numerical order. Variables can be repeated if required. """

        self._params['sequence'] = sequence
        self._params['value_means'] = value_means
        self._params['value_ranges'] = value_ranges
        self._params['arrangement'] = arrangement
        if any(x <= 0 for x in self._params['value_ranges']):
            raise ValueError("range values must be greater than zero")
        self._params['variable_parameters'] = []
        for i in range(len(self._params['value_means'])):
            self._params['variable_parameters'].append(
                "".join(['var', str(i)]))
        if len(set(arrangement).intersection(
                self._params['variable_parameters'])) != len(
                    self._params['value_means']):
            raise ValueError("argument mismatch!")
        if len(self._params['value_ranges']) != len(
                self._params['value_means']):
            raise ValueError("argument mismatch!")