Cleaning up CodeFactor issues

saforem2 · Apr 14, 2022 · e5b52da · e5b52da
1 parent def991a
commit e5b52da
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Showing 2 changed files with 11 additions and 11 deletions.
diff --git a/src/l2hmc/group/pytorch/group.py b/src/l2hmc/group/pytorch/group.py
@@ -273,11 +273,11 @@ def SU3Gradient(
     """
     x.requires_grad_(True)
     y = f(x)
-    id = torch.ones(x.shape[0], device=x.device)
+    identity = torch.ones(x.shape[0], device=x.device)
     dydx, = torch.autograd.grad(y, x,
                                 create_graph=create_graph,
                                 retain_graph=True,
-                                grad_outputs=id)
+                                grad_outputs=identity)
     return y, dydx
 
 
@@ -292,9 +292,9 @@ def mul(
     ) -> Tensor:
         if adjoint_a and adjoint_b:
             return torch.matmul(a.adjoint(), b.adjoint())
-        elif adjoint_a:
+        if adjoint_a:
             return torch.matmul(a.adjoint(), b)
-        elif adjoint_b:
+        if adjoint_b:
             return torch.matmul(a, b.adjoint())
         return torch.matmul(a, b)
 
@@ -309,9 +309,9 @@ def mul(
     ) -> Tensor:
         if adjoint_a and adjoint_b:
             return -a - b
-        elif adjoint_a:
+        if adjoint_a:
             return -a + b
-        elif adjoint_b:
+        if adjoint_b:
             return a - b
         return a + b
 
@@ -354,9 +354,9 @@ def mul(
     ) -> Tensor:
         if adjoint_a and adjoint_b:
             return torch.matmul(a.adjoint(), b.adjoint())
-        elif adjoint_a:
+        if adjoint_a:
             return torch.matmul(a.adjoint(), b)
-        elif adjoint_b:
+        if adjoint_b:
             return torch.matmul(a, b.adjoint())
         return torch.matmul(a, b)
 

diff --git a/src/l2hmc/lattice/u1/pytorch/lattice.py b/src/l2hmc/lattice/u1/pytorch/lattice.py
@@ -72,11 +72,11 @@ def grad_action(
         """Compute the gradient of the potential function."""
         x.requires_grad_(True)
         s = self.action(x, beta)
-        id = torch.ones(x.shape[0], device=x.device)
+        identity = torch.ones(x.shape[0], device=x.device)
         dsdx, = torch.autograd.grad(s, x,
-                                    create_graph=create_graph,
                                     retain_graph=True,
-                                    grad_outputs=id)
+                                    create_graph=create_graph,
+                                    grad_outputs=identity)
         return dsdx
 
     def plaqs_diff(