diff --git a/examples/models/llama/source_transformation/apply_spin_quant_r1_r2.py b/examples/models/llama/source_transformation/apply_spin_quant_r1_r2.py
index 7ec35c7b6c9..89f564935fc 100644
--- a/examples/models/llama/source_transformation/apply_spin_quant_r1_r2.py
+++ b/examples/models/llama/source_transformation/apply_spin_quant_r1_r2.py
@@ -146,9 +146,9 @@ def fuse_ln_linear(
                     torch.zeros(linear.out_features, dtype=torch.float32)
                 )
             linear.bias.data = linear.bias.data.to(dtype=torch.float32) + torch.matmul(
+                W_,
                 # pyre-fixme[6]: For 2nd argument expected `Tensor` but got
                 #  `Union[Tensor, Module]`.
-                W_,
                 layernorm.bias.to(dtype=torch.float32),
             )
             linear.bias.data = linear.bias.data.to(linear_dtype)
diff --git a/exir/emit/_emitter.py b/exir/emit/_emitter.py
index a1dcc23dcee..381bab618cd 100644
--- a/exir/emit/_emitter.py
+++ b/exir/emit/_emitter.py
@@ -1634,8 +1634,8 @@ def plan(self) -> ExecutionPlan:
             # missing in scenarios like unit test that does not enable memory planning, assume an
             # empty list.
             non_const_buffer_sizes=typing.cast(
-                # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorB...
                 List[int],
+                # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorB...
                 self.module.meta["non_const_buffer_sizes"],
             ),
             container_meta_type=self.container_meta_type,