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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 random_reset_mutation(random, candidate, args):
 """Return the mutants produced by randomly choosing new values. This function performs random-reset mutation. It assumes that candidate solutions are composed of discrete values. This function makes use of the bounder function as specified in the EC's ``evolve`` method, and it assumes that the bounder contains an attribute called *values* (which is true for instances of ``DiscreteBounder``). The mutation moves through a candidate solution and, with rate equal to the *mutation_rate*, randomly chooses a value from the set of allowed values to be used in that location. Note that this value may be the same as the original value. .. Arguments: random -- the random number generator object candidate -- the candidate solution args -- a dictionary of keyword arguments Optional keyword arguments in args: - *mutation_rate* -- the rate at which mutation is performed (default 0.1) The mutation rate is applied on an element by element basis. """

    bounder = args['_ec'].bounder
    try:
        values = bounder.values
    except AttributeError:
        values = None
    if values is not None:
        rate = args.setdefault('mutation_rate', 0.1)
        mutant = copy.copy(candidate)
        for i, m in enumerate(mutant):
            if random.random() < rate:
                mutant[i] = random.choice(values)
        return mutant
    else:
        return candidate