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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 reset_parameter(**kwargs):
 """Create a callback that resets the parameter after the first iteration. Note ---- The initial parameter will still take in-effect on first iteration. Parameters **kwargs : value should be list or function List of parameters for each boosting round or a customized function that calculates the parameter in terms of current number of round (e.g. yields learning rate decay). If list lst, parameter = lst[current_round]. If function func, parameter = func(current_round). Returns ------- callback : function The callback that resets the parameter after the first iteration. """

    def _callback(env):
        new_parameters = {}
        for key, value in kwargs.items():
            if key in ['num_class', 'num_classes',
                       'boosting', 'boost', 'boosting_type',
                       'metric', 'metrics', 'metric_types']:
                raise RuntimeError("cannot reset {} during training".format(repr(key)))
            if isinstance(value, list):
                if len(value) != env.end_iteration - env.begin_iteration:
                    raise ValueError("Length of list {} has to equal to 'num_boost_round'."
                                     .format(repr(key)))
                new_param = value[env.iteration - env.begin_iteration]
            else:
                new_param = value(env.iteration - env.begin_iteration)
            if new_param != env.params.get(key, None):
                new_parameters[key] = new_param
        if new_parameters:
            env.model.reset_parameter(new_parameters)
            env.params.update(new_parameters)
    _callback.before_iteration = True
    _callback.order = 10
    return _callback