How does the meta Random Forest Classifier determine the final classification?
Each data point $x$ that you are predicting necessarily only lands in one leaf node for each tree. So if I have N trees, and $K_A$ is the number of trees in which $x$ ends up in a leaf node which predicts A, then we can say $x$ is class $A$ with "probability" $K_A/N$, and similarly for other classes. Then you can just make an argmax prediction for which class $x$ belongs to based on the "probabilities" (I keep putting this in quotes because it isn't an actual probability, but it's sometimes ok to think of it this way.)