diff --git a/tests/unfuse_batch_norm.mlir b/tests/unfuse_batch_norm.mlir
index 867c1d2..c193072 100644
--- a/tests/unfuse_batch_norm.mlir
+++ b/tests/unfuse_batch_norm.mlir
@@ -110,7 +110,7 @@ func @batchNormInference_dynamic_shape(
   // CHECK-DAG: %[[EPS:.+]] = mhlo.constant dense<1.000000e-03> : tensor<f32>
   // CHECK-DAG: %[[DIM:.+]] = dim %[[VARIANCE]], %[[C0]] : tensor<?xf32>
   // CHECK-DAG: %[[TO_DIM_TENSOR:.+]] = tensor_from_elements(%[[DIM]]) : tensor<1xindex>
-  // CHECK-DAG: %[[EPS_BCAST:.+]] =  "mhlo.dynamic_broadcast_in_dim"(%[[EPS]], %[[TO_DIM_TENSOR]]) {broadcast_dimensions = dense<[]> : tensor<0xi64>} : (tensor<f32>, tensor<1xindex>) -> tensor<?xf32>
+  // CHECK-DAG: %[[EPS_BCAST:.+]] =  "mhlo.dynamic_broadcast_in_dim"(%[[EPS]], %[[TO_DIM_TENSOR]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>, tensor<1xindex>) -> tensor<?xf32>
   // CHECK-DAG: %[[VARIANCE_EPS:.+]] = mhlo.add %[[VARIANCE]], %[[EPS_BCAST]] : tensor<?xf32>
   // CHECK-DAG: %[[STDDEV:.+]] = "mhlo.sqrt"(%[[VARIANCE_EPS]]) : (tensor<?xf32>) -> tensor<?xf32>
   // CHECK-DAG: %[[INPUT_DIM_0:.+]] = dim %[[X]], %[[C0]] : tensor<?x?x?x?xf32>