Fix signatures of TorchModel.cross_validate

Summary:
Some subclasses were missing SSD input, which leads to errors when called from `TorchAdapter` as
```
        f_test, cov_test = none_throws(self.model).cross_validate(
            datasets=datasets,
            X_test=torch.as_tensor(X_test, dtype=self.dtype, device=self.device),
            search_space_digest=search_space_digest,
            use_posterior_predictive=use_posterior_predictive,
        )
```
This was something raised by pyre but it was being suppressed.

Differential Revision: D69951061

ax/models/torch/botorch.py

@@ -468,10 +468,11 @@ def best_point(
         )
 
     @copy_doc(TorchGenerator.cross_validate)
-    def cross_validate(  # pyre-ignore [14]: `search_space_digest` arg not needed here
+    def cross_validate(
         self,
         datasets: list[SupervisedDataset],
         X_test: Tensor,
+        search_space_digest: SearchSpaceDigest,
         use_posterior_predictive: bool = False,
     ) -> tuple[Tensor, Tensor]:
         if self._model is None:

ax/models/torch/randomforest.py

@@ -66,10 +66,11 @@ def predict(self, X: Tensor) -> tuple[Tensor, Tensor]:
         return _rf_predict(self.models, X)
 
     @copy_doc(TorchGenerator.cross_validate)
-    def cross_validate(  # pyre-ignore [14]: not using metric_names or ssd
+    def cross_validate(
         self,
         datasets: list[SupervisedDataset],
         X_test: Tensor,
+        search_space_digest: SearchSpaceDigest,
         use_posterior_predictive: bool = False,
     ) -> tuple[Tensor, Tensor]:
         Xs, Ys, Yvars = _datasets_to_legacy_inputs(datasets=datasets)