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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 early_stopping(stopping_rounds, first_metric_only=False, verbose=True):
 """Create a callback that activates early stopping. Note ---- Activates early stopping. The model will train until the validation score stops improving. Validation score needs to improve at least every ``early_stopping_rounds`` round(s) to continue training. Requires at least one validation data and one metric. If there's more than one, will check all of them. But the training data is ignored anyway. To check only the first metric set ``first_metric_only`` to True. Parameters stopping_rounds : int The possible number of rounds without the trend occurrence. first_metric_only : bool, optional (default=False) Whether to use only the first metric for early stopping. verbose : bool, optional (default=True) Whether to print message with early stopping information. Returns ------- callback : function The callback that activates early stopping. """

    best_score = []
    best_iter = []
    best_score_list = []
    cmp_op = []
    enabled = [True]

    def _init(env):
        enabled[0] = not any((boost_alias in env.params
                              and env.params[boost_alias] == 'dart') for boost_alias in ('boosting',
                                                                                         'boosting_type',
                                                                                         'boost'))
        if not enabled[0]:
            warnings.warn('Early stopping is not available in dart mode')
            return
        if not env.evaluation_result_list:
            raise ValueError('For early stopping, '
                             'at least one dataset and eval metric is required for evaluation')

        if verbose:
            msg = "Training until validation scores don't improve for {} rounds."
            print(msg.format(stopping_rounds))

        for eval_ret in env.evaluation_result_list:
            best_iter.append(0)
            best_score_list.append(None)
            if eval_ret[3]:
                best_score.append(float('-inf'))
                cmp_op.append(gt)
            else:
                best_score.append(float('inf'))
                cmp_op.append(lt)

    def _callback(env):
        if not cmp_op:
            _init(env)
        if not enabled[0]:
            return
        for i in range_(len(env.evaluation_result_list)):
            score = env.evaluation_result_list[i][2]
            if best_score_list[i] is None or cmp_op[i](score, best_score[i]):
                best_score[i] = score
                best_iter[i] = env.iteration
                best_score_list[i] = env.evaluation_result_list
            elif env.iteration - best_iter[i] >= stopping_rounds:
                if verbose:
                    print('Early stopping, best iteration is:\n[%d]\t%s' % (
                        best_iter[i] + 1, '\t'.join([_format_eval_result(x) for x in best_score_list[i]])))
                raise EarlyStopException(best_iter[i], best_score_list[i])
            if env.iteration == env.end_iteration - 1:
                if verbose:
                    print('Did not meet early stopping. Best iteration is:\n[%d]\t%s' % (
                        best_iter[i] + 1, '\t'.join([_format_eval_result(x) for x in best_score_list[i]])))
                raise EarlyStopException(best_iter[i], best_score_list[i])
            if first_metric_only:  # the only first metric is used for early stopping
                break
    _callback.order = 30
    return _callback