Fix failing test

DenseLinearOperator expects a torch.tensor so we convert the linear operator to dense.

Author: naefjo

gpytorch/models/exact_prediction_strategies.py

@@ -222,7 +222,7 @@ def get_fantasy_strategy(self, inputs, targets, full_inputs, full_targets, full_
             full_inputs = [fi.expand(fant_batch_shape + fi.shape) for fi in full_inputs]
             full_mean = full_mean.expand(fant_batch_shape + full_mean.shape)
             full_covar = BatchRepeatLinearOperator(full_covar, repeat_shape)
-            new_root = BatchRepeatLinearOperator(DenseLinearOperator(new_root), repeat_shape)
+            new_root = BatchRepeatLinearOperator(DenseLinearOperator(new_root.to_dense()), repeat_shape)
             # no need to repeat the covar cache, broadcasting will do the right thing
 
         if isinstance(full_output, MultitaskMultivariateNormal):