What happens to test_cv_fold when using "weight" to form the train dmatrix.

Hello Community,
I had a doubt which I would be very happy if anybody can help.
When forming the dmatrix of the train dataset I used the parameter "weight" to set the weight of each sample in my training data. Now, I intend to use the xgboost.cv() feature to cross_validate my model . But I'm not confident how model treats the test_fold in this case due to "weight" parameter.

This is what my thinking is. "weight" parameter has only role to play while constructing the trees, and once our model is ready, it predicts the new incoming data based on the constructed tree. So if I use 5 fold cross_validation, "weight" will play role in only 4 train_folds , while the 5th testing_fold would be judged only on the basis of True_labels and predicted_labels and so "weight" wouldn't have any role to play there.

Please comment your views upon the same. Thankyou everybody in advance. 😄