prevent warning (#3078)

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## Motivation

Fixes the issue described in #3071.

### Have you read the [Contributing Guidelines on pull requests](https://github.com/meta-pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes.

Pull Request resolved: #3078

Test Plan: Unit tests.

Reviewed By: hvarfner

Differential Revision: D86792344

Pulled By: Balandat

fbshipit-source-id: 6d5e4886b81e304efbea814847b3fe03674c799c

Author: jduerholt

botorch/models/deterministic.py

@@ -40,6 +40,12 @@
 class DeterministicModel(EnsembleModel):
     """Abstract base class for deterministic models."""
 
+    def _set_transformed_inputs(self):
+        """Overwrites the parent method to prevent raise of
+        warning "Could not update `train_inputs` with transformed inputs."
+        """
+        return None
+
     @abstractmethod
     def forward(self, X: Tensor) -> Tensor:
         r"""Compute the (deterministic) model output at X.

test/models/test_deterministic.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-import warnings
 
 import torch
 from botorch.acquisition.objective import ScalarizedPosteriorTransform
@@ -149,15 +148,11 @@ def test_with_transforms(self):
         # check that the posterior output agrees with the manually transformed one
         test_X = torch.rand(3, dim)
         expected_Y, _ = octf.untransform(model.forward(intf(test_X)))
-        with warnings.catch_warnings(record=True) as ws:
-            posterior = model.posterior(test_X)
-            msg = "does not have a `train_inputs` attribute"
-            self.assertTrue(any(msg in str(w.message) for w in ws))
+        posterior = model.posterior(test_X)
         self.assertAllClose(expected_Y, posterior.mean)
-        # check that model.train/eval works and raises the warning
+        # check that model.train/eval works
         model.train()
-        with self.assertWarns(RuntimeWarning):
-            model.eval()
+        model.eval()
 
     def test_posterior_transform(self):
         def f(X):