Make variance optional for TransformedPosterior.mean call

botorch/posteriors/transformed.py

@@ -68,7 +68,11 @@ def mean(self) -> Tensor:
         r"""The mean of the posterior as a `batch_shape x n x m`-dim Tensor."""
         if self._mean_transform is None:
             raise NotImplementedError("No mean transform provided.")
-        return self._mean_transform(self._posterior.mean, self._posterior.variance)
+        try:
+            variance = self._posterior.variance
+        except (NotImplementedError, AttributeError):
+            variance = None
+        return self._mean_transform(self._posterior.mean, variance)
 
     @property
     def variance(self) -> Tensor:

test/posteriors/test_transformed.py

@@ -69,3 +69,22 @@ def test_transformed_posterior(self):
                     p_tf_2.mean
                 with self.assertRaises(NotImplementedError):
                     p_tf_2.variance
+
+        # check that `mean` works even if posterior doesn't have `variance`
+        for error in (AttributeError, NotImplementedError):
+
+            class DummyPosterior(object):
+                mean = torch.zeros(5)
+
+                @property
+                def variance(self):
+                    raise error
+
+            post = DummyPosterior()
+            transformed_post = TransformedPosterior(
+                posterior=post,
+                sample_transform=None,
+                mean_transform=lambda x, _: x + 1,
+            )
+            transformed_mean = transformed_post.mean
+            self.assertTrue(torch.allclose(transformed_mean, torch.ones(5)))