[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

deepmd/pt_expt/model/get_model.py

@@ -12,9 +12,7 @@
     TYPE_CHECKING,
 )
 
-from deepmd.dpmodel.atomic_model.dp_atomic_model import (
-    DPAtomicModel as DPAtomicModelDP,
-)
+from deepmd.dpmodel.atomic_model.dp_atomic_model import DPAtomicModel as DPAtomicModelDP
 from deepmd.dpmodel.atomic_model.inner_potential import (
     InnerPotentialAtomicModel as InnerPotentialAtomicModelDP,
 )
@@ -24,12 +22,12 @@
 from deepmd.dpmodel.model.model_factory import (
     BackendModelFactory,
 )
-from deepmd.pt_expt.common import (
-    auto_wrapped_class,
-)
 from deepmd.dpmodel.model.model_factory import (
     get_spin_model as get_spin_model_from_factory,
 )
+from deepmd.pt_expt.common import (
+    auto_wrapped_class,
+)
 from deepmd.pt_expt.descriptor import (
     BaseDescriptor,
 )

source/tests/common/dpmodel/test_dpa4_frame_mixers.py

@@ -276,15 +276,11 @@ def test_focus_batched_lowering_matches_einsum_backward(kind) -> None:
     rng = np.random.default_rng(2026)
 
     if kind == "contract":
-        from deepmd.pt.model.descriptor.sezm_nn.grid_net import (
-            FrameContract as PTMixer,
-        )
+        from deepmd.pt.model.descriptor.sezm_nn.grid_net import FrameContract as PTMixer
 
         in_dim = n_frames * channels
     else:
-        from deepmd.pt.model.descriptor.sezm_nn.grid_net import (
-            FrameExpand as PTMixer,
-        )
+        from deepmd.pt.model.descriptor.sezm_nn.grid_net import FrameExpand as PTMixer
 
         in_dim = channels
     pt_mod = PTMixer(
@@ -326,12 +322,12 @@ def test_focus_batched_lowering_matches_einsum_backward(kind) -> None:
     )
 
     # the dpmodel lowering agrees on the torch namespace, gradients included
+    import array_api_compat
+
     from deepmd.dpmodel.descriptor.dpa4_nn.grid_net import (
         _degree_batched_matmul,
     )
 
-    import array_api_compat
-
     coeff_dp = coeff.detach().clone().requires_grad_(True)
     dp_out = _degree_batched_matmul(
         array_api_compat.array_namespace(coeff_dp),