Creating an additional layer would initialize all parameters (assuming this layer contains parameters) by sampling random numbers through the pseudorandom number generator (PRNG). This would affect all future calls of the PRNG and would not yield the same results even if the additional layer is not used in the forward. Changing the seed would have a similar effect and if your model is sensitive to the seed it would mean that the overall training is not particularly stable.