diff --git a/torchao/dtypes/uintx/semi_sparse_layout.py b/torchao/dtypes/uintx/semi_sparse_layout.py
index d832731657..a554fd9bc6 100644
--- a/torchao/dtypes/uintx/semi_sparse_layout.py
+++ b/torchao/dtypes/uintx/semi_sparse_layout.py
@@ -44,6 +44,7 @@ def _linear_int8_act_int8_weight_semi_structured_sparse_impl(
     # must pad
     row, col = tmp.shape
     from torch.sparse import SparseSemiStructuredTensorCUSPARSELT
+
     tmp_padded = SparseSemiStructuredTensorCUSPARSELT._pad_dense_input(tmp)
     # we fuse one of the scalar matrix multiplications (w_scales) into the sparse mm
     y_dot_bf16_w_scales_fused = torch._cslt_sparse_mm(
@@ -51,7 +52,7 @@ def _linear_int8_act_int8_weight_semi_structured_sparse_impl(
         tmp_padded.t(),
         alpha=w_scales.to(torch.float32),
         out_dtype=torch.bfloat16,
-        ).t()[:row, :]
+    ).t()[:row, :]
     y = (y_dot_bf16_w_scales_fused * x_scales.reshape(-1, 1)).reshape(
         *x_vals_int8.shape[:-1], y_dot_bf16_w_scales_fused.shape[-1]
     )