Could you explain your use case a bit more?
As far as I understand it, you would like to create a linear layer with the same number of input and output features (i.e. 200 in your case) and zero out the activations where the features of your input were “missing”.
Note that a linear layer creates a fully connected weight matrix, such that each input features will be multiplied with a certain weight to create each output activation.
I’m not sure how using dropout at specific indices should help. Do you have a reference paper for this method?