What's the purpose of learning rate in sklearn AdaBoost implementation

We know that sklearn's implemenation of AdaBoost algorithm uses DecisionTreeClassifier as the base learner.

Conceptually, AdaBoost assigns equal weights to all examples initially, where the weight is equal to 1/n. n- the number of examples.

But then the AdaBoost documentantion includes a hyperparameter learning_rate defined as:

learning_rate float, default=1.

Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the learning_rate and n_estimators parameters.

But then Decision Trees do not use learning_rate parameter since it not a gradient-based model learning approach. Besides, AdaBoost already assigns 1/n to each sample. How then the definition of learning_rate defined in the documentation fits here?