FullyBayesianSingleTaskGP.train should not return None

Summary: This is for consistency with the signature of `Module.train`.

Differential Revision: D68710923

botorch/models/fully_bayesian.py

@@ -33,7 +33,7 @@
 import math
 from abc import ABC, abstractmethod
 from collections.abc import Mapping
-from typing import Any
+from typing import Any, TypeVar
 
 import pyro
 import torch
@@ -67,6 +67,11 @@
 from pyro.ops.integrator import register_exception_handler
 from torch import Tensor
 
+# Can replace with Self type once 3.11 is the minimum version
+TFullyBayesianSingleTaskGP = TypeVar(
+    "TFullyBayesianSingleTaskGP", bound="FullyBayesianSingleTaskGP"
+)
+
 _sqrt5 = math.sqrt(5)
 
 
@@ -623,13 +628,16 @@ def _aug_batch_shape(self) -> torch.Size:
             aug_batch_shape += torch.Size([self.num_outputs])
         return aug_batch_shape
 
-    def train(self, mode: bool = True) -> None:
+    def train(
+        self: TFullyBayesianSingleTaskGP, mode: bool = True
+    ) -> TFullyBayesianSingleTaskGP:
         r"""Puts the model in `train` mode."""
         super().train(mode=mode)
         if mode:
             self.mean_module = None
             self.covar_module = None
             self.likelihood = None
+        return self
 
     def load_mcmc_samples(self, mcmc_samples: dict[str, Tensor]) -> None:
         r"""Load the MCMC hyperparameter samples into the model.

test/models/test_fully_bayesian.py

@@ -434,7 +434,8 @@ def test_fit_model(self):
             # Make sure the model shapes are set correctly
             self.assertEqual(model.pyro_model.train_X.shape, torch.Size([n, d]))
             self.assertAllClose(model.pyro_model.train_X, train_X)
-            model.train()  # Put the model in train mode
+            trained_model = model.train()  # Put the model in train mode
+            self.assertIs(trained_model, model)
             self.assertAllClose(train_X, model.pyro_model.train_X)
             self.assertIsNone(model.mean_module)
             self.assertIsNone(model.covar_module)