lint with ufmt

botorch/optim/optimize_homotopy.py

@@ -5,21 +5,19 @@
 
 from __future__ import annotations
 
+from collections.abc import Callable
+
 from typing import Any
 
 import torch
 from botorch.acquisition import AcquisitionFunction
+
+from botorch.generation.gen import TGenCandidates
 from botorch.optim.homotopy import Homotopy
+from botorch.optim.initializers import TGenInitialConditions
 from botorch.optim.optimize import optimize_acqf
 from torch import Tensor
 
-from collections.abc import Callable
-
-from botorch.generation.gen import TGenCandidates
-from botorch.optim.initializers import (
-    TGenInitialConditions,
-)
-
 
 def prune_candidates(
     candidates: Tensor, acq_values: Tensor, prune_tolerance: float
@@ -188,7 +186,7 @@ def optimize_acqf_homotopy(
                 q=1,
                 options=options,
                 batch_initial_conditions=candidates,
-                **shared_optimize_acqf_kwargs
+                **shared_optimize_acqf_kwargs,
             )
             homotopy.step()
 

test/optim/test_homotopy.py

@@ -158,11 +158,13 @@ def test_optimize_acqf_homotopy(self):
         self.assertEqual(acqf_val.shape, torch.Size([3]))
 
         # with linear constraints
-        constraints = [(  # X[..., 0] + X[..., 1] >= 2.
-            torch.tensor([0, 1], device=self.device),
-            torch.ones(2, device=self.device, dtype=torch.double),
-            2.0,
-        )]
+        constraints = [
+            (  # X[..., 0] + X[..., 1] >= 2.
+                torch.tensor([0, 1], device=self.device),
+                torch.ones(2, device=self.device, dtype=torch.double),
+                2.0,
+            )
+        ]
 
         acqf = PosteriorMean(model=model)
         candidate, acqf_val = optimize_acqf_homotopy(