That's not easily possible with the same likelihood (the default one is homoskedastic). You could instead use a FixedNoiseGaussianLikelihood where you instantiate the first part of the vector of noises to the inferred noise level of your model and set the other ones to a very small noise level.
This is a comment from here: https://github.com/cornellius-gp/gpytorch/issues/645
That's not easily possible with the same likelihood (the default one is homoskedastic). You could instead use a FixedNoiseGaussianLikelihood where you instantiate the first part of the vector of noises to the inferred noise level of your model and set the other ones to a very small noise level.