text: Title: Deterministic evaluation of probabilistic models (e.g. nbeats)
  Body: When predicting a time series, one can turn some models probabilistic by defining a likelihood parameter.
  E.g.: 
  NBEATSModel(
      ...
      likelihood=GaussianLikelihood(),
      random_state=42
  ) 
  
  If done this way, the prediction is not deterministic any more. Two subsequent calls to 
  predict(series=.., n=1, num_samples=1)
  yield different results.
  
  For developing purposes, it would be nice to be able to fix the random state of the likelihood as well so that, independent of the num_samples defined, the result of the prediction always stays the same.