Robust Gaussian Processes via Relevance Pursuit

botorch/models/gpytorch.py

@@ -387,21 +387,23 @@ def _apply_noise(
                 obs_noise = observation_noise.squeeze(-1)
             mvn = self.likelihood(
                 mvn,
-                X,
+                [X],
                 noise=obs_noise.expand(noise_shape),
             )
         elif isinstance(self.likelihood, FixedNoiseGaussianLikelihood):
             # Use the mean of the previous noise values (TODO: be smarter here).
             observation_noise = self.likelihood.noise.mean(dim=-1, keepdim=True)
             mvn = self.likelihood(
                 mvn,
-                X,
+                [X],
                 noise=observation_noise.expand(noise_shape),
             )
         else:
-            mvn = self.likelihood(mvn, X)
+            mvn = self.likelihood(mvn, [X])
         return mvn
 
+    # pyre-ignore[14]: Inconsistent override. Could not find parameter
+    # `Keywords(typing.Any)` in overriding signature.
     def posterior(
         self,
         X: Tensor,
@@ -470,6 +472,7 @@ def posterior(
             return posterior_transform(posterior)
         return posterior
 
+    # pyre-ignore[14]: Inconsistent override. Could not find parameter `noise`.
     def condition_on_observations(
         self, X: Tensor, Y: Tensor, **kwargs: Any
     ) -> BatchedMultiOutputGPyTorchModel:
@@ -632,7 +635,7 @@ def batch_shape(self) -> torch.Size:
                 raise NotImplementedError(msg + " that are not broadcastble.")
         return next(iter(batch_shapes))
 
-    # pyre-fixme[15]: Inconsistent override in return types
+    # pyre-fixme[14]: Inconsistent override in return types
     def posterior(
         self,
         X: Tensor,
@@ -838,6 +841,8 @@ def _apply_noise(
             )
         return self.likelihood(mvn, X)
 
+    # pyre-ignore[14]: Inconsistent override. Could not find parameter
+    # `Keywords(typing.Any)` in overriding signature.
     def posterior(
         self,
         X: Tensor,