used clang format to clean up

src/targets/cpu/cpu_lowering.cpp

@@ -51,9 +51,10 @@ struct cpu_batch_norm_inference
         auto gamma               = args[3].at<float>();
         auto beta                = args[4].at<float>();
 
-	    visit_all(output, input) ([&](auto result, auto buffer) {
+        visit_all(output, input)([&](auto result, auto buffer) {
             std::transform(buffer.begin(), buffer.end(), result.begin(), [&](auto x) {
-                return gamma * (x - mini_batch_mean) / std::sqrt(mini_batch_variance + epsilon) + beta;
+                return gamma * (x - mini_batch_mean) / std::sqrt(mini_batch_variance + epsilon) +
+                       beta;
             });
         });
 
@@ -515,7 +516,8 @@ struct cpu_apply
     {
         apply_map["convolution"] = extend_op<cpu_convolution, convolution>();
         apply_map["gemm"]        = extend_op<cpu_gemm, gemm>();
-        apply_map["batch_norm_inference"]        = extend_op<cpu_batch_norm_inference, batch_norm_inference>();
+        apply_map["batch_norm_inference"] =
+            extend_op<cpu_batch_norm_inference, batch_norm_inference>();
         apply_map["reshape"]    = extend_op<cpu_reshape, reshape>();
         apply_map["contiguous"] = extend_op<cpu_contiguous, contiguous>();
         apply_map["transpose"]  = extend_op<cpu_transpose, transpose>();

test/cpu_ops_test.cpp

@@ -19,8 +19,9 @@ void batch_norm_inference_test()
     p.compile(migraph::cpu::cpu_target{});
     auto result = p.eval({});
     std::vector<float> result_vector(4);
-    result.visit([&](auto output) {result_vector.assign(output.begin(), output.end()); });
-    std::vector<float> gold = { 1 / (1 + 1.0e-6), 2 / (1 + 1.0e-6), 3 / (1 + 1.0e-6), 4 / (1 + 1.0e-6)};
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold = {
+        1 / (1 + 1.0e-6), 2 / (1 + 1.0e-6), 3 / (1 + 1.0e-6), 4 / (1 + 1.0e-6)};
     EXPECT(test::verify_range(result_vector, gold));
 }