diff --git a/tests/Examples/micro_speech/micro_speech.tosa.mlir b/tests/Examples/micro_speech/micro_speech.tosa.mlir
index 00e4ef494..02958bfac 100644
--- a/tests/Examples/micro_speech/micro_speech.tosa.mlir
+++ b/tests/Examples/micro_speech/micro_speech.tosa.mlir
@@ -28,6 +28,8 @@ module attributes {tfl.description = "TOCO Converted.", tfl.schema_version = 3 :
     %15 = "tosa.const"() <{value = dense<[427, -518, -94, 186]> : tensor<4xi32>}> : () -> tensor<4xi32>
     %16 = "tosa.const"() <{value = dense<"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tensor<4x4000xi8>}> : () -> tensor<4x4000xi8>
     %17 = "tosa.const"() <{value = dense<[-374, 169, -48, 208, 82, 6, -1201, -694]> : tensor<8xi32>}> : () -> tensor<8xi32>
+    %shift = "tosa.const"() {value = dense<31> : tensor<1xi8>} : () -> tensor<1xi8>
+    %shift30 = "tosa.const"() {value = dense<30> : tensor<1xi8>} : () -> tensor<1xi8>
     %18 = "tosa.reshape"(%arg0) <{new_shape = array<i64: 1, 49, 40, 1>}> : (tensor<1x1960xi8>) -> tensor<1x49x40x1xi8>
     %19 = "tosa.depthwise_conv2d"(%18, %0, %17) <{acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 4, 5, 3, 3>, quantization_info = #tosa.conv_quant<input_zp = -128, weight_zp = 0>, stride = array<i64: 2, 2>}> : (tensor<1x49x40x1xi8>, tensor<10x8x1x8xi8>, tensor<8xi32>) -> tensor<1x25x20x8xi32>
     %20 = "tosa.rescale"(%19) <{double_round = true, input_zp = 0 : i32, multiplier = array<i32: 1653229999, 1516545207, 2000799311, 1159928266, 1498403863, 1285645282, 2146175029, 1756589032>, output_zp = -128 : i32, per_channel = true, scale32 = true, shift = array<i8: 41, 43, 41, 41, 41, 41, 41, 41>}> : (tensor<1x25x20x8xi32>) -> tensor<1x25x20x8xi8>
@@ -55,24 +57,24 @@ module attributes {tfl.description = "TOCO Converted.", tfl.schema_version = 3 :
     %42 = "tosa.clz"(%41) : (tensor<1x1xi32>) -> tensor<1x1xi32>
     %43 = "tosa.sub"(%42, %3) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
     %44 = "tosa.logical_left_shift"(%41, %43) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %45 = "tosa.mul"(%44, %6) <{shift = 31 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
+    %45 = "tosa.mul"(%44, %6, %shift) : (tensor<1x1xi32>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<1x1xi32>
     %46 = "tosa.add"(%45, %7) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %47 = "tosa.mul"(%46, %44) <{shift = 31 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
+    %47 = "tosa.mul"(%46, %44, %shift) : (tensor<1x1xi32>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<1x1xi32>
     %48 = "tosa.sub"(%8, %47) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %49 = "tosa.mul"(%46, %48) <{shift = 31 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %50 = "tosa.mul"(%49, %9) <{shift = 0 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
+    %49 = "tosa.mul"(%46, %48, %shift) : (tensor<1x1xi32>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<1x1xi32>
+    %50 = "tosa.mul"(%49, %9) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
     %51 = "tosa.add"(%46, %50) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %52 = "tosa.mul"(%51, %44) <{shift = 31 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
+    %52 = "tosa.mul"(%51, %44, %shift) : (tensor<1x1xi32>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<1x1xi32>
     %53 = "tosa.sub"(%8, %52) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %54 = "tosa.mul"(%51, %53) <{shift = 31 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %55 = "tosa.mul"(%54, %9) <{shift = 0 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
+    %54 = "tosa.mul"(%51, %53, %shift) : (tensor<1x1xi32>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<1x1xi32>
+    %55 = "tosa.mul"(%54, %9) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
     %56 = "tosa.add"(%51, %55) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %57 = "tosa.mul"(%56, %44) <{shift = 31 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
+    %57 = "tosa.mul"(%56, %44, %shift) : (tensor<1x1xi32>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<1x1xi32>
     %58 = "tosa.sub"(%8, %57) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %59 = "tosa.mul"(%56, %58) <{shift = 31 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %60 = "tosa.mul"(%59, %9) <{shift = 0 : i8}> : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
+    %59 = "tosa.mul"(%56, %58, %shift) : (tensor<1x1xi32>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<1x1xi32>
+    %60 = "tosa.mul"(%59, %9) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
     %61 = "tosa.add"(%56, %60) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
-    %62 = "tosa.mul"(%39, %61) <{shift = 30 : i8}> : (tensor<1x4xi32>, tensor<1x1xi32>) -> tensor<1x4xi32>
+    %62 = "tosa.mul"(%39, %61, %shift30) : (tensor<1x4xi32>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<1x4xi32>
     %63 = "tosa.sub"(%10, %42) : (tensor<1x1xi32>, tensor<1x1xi32>) -> tensor<1x1xi32>
     %64 = "tosa.arithmetic_right_shift"(%62, %63) <{round = true}> : (tensor<1x4xi32>, tensor<1x1xi32>) -> tensor<1x4xi32>
     %65 = "tosa.rescale"(%64) <{double_round = false, input_zp = 0 : i32, multiplier = array<i32: 1073741824>, output_zp = -128 : i32, per_channel = false, scale32 = true, shift = array<i8: 30>}> : (tensor<1x4xi32>) -> tensor<1x4xi8>