And because the model thinks C - one is important. After you train the model , you sort of  y you could do  you could do still what I was proposing initially , that during the training you  you compress C - one that becomes  then it becomes less important in a training.
Mm - hmm.
But if you have  if you want to run e ex extensive experiment without retraining the model , you don't have to retrain the model. You train it on the original vector. But after , you  wh when you are doing this parametric study of importance of C - one you will de - weight the C - one component in the model , and you will put in the  you will compress the  this component in a  in the test data. s by the same amount.
Could you also if you wanted to  if you wanted to try an experiment uh by  leaving out say , C - one , couldn't you , in your test data , uh modify the  all of the C - one values to be um way outside of the normal range of the Gaussian for C - one that was trained in the model ? So that effectively , the C - one never really contributes to the score ?