%0 = arith.constant dense<-1.0> : tensor<f32>
  %1 = arith.constant dense<1.0> : tensor<f32>
  %2 = arith.constant dense<1.0> : tensor<f32>
  %3 = arith.constant dense<1.0> : tensor<f32>
  %4 = arith.constant dense<4.0> : tensor<f32>
  %5 = arith.constant dense<4.0> : tensor<f32>
  %6 = arith.constant dense<2.0> : tensor<f32>

  // CHECK-DAG: [[cst0:%.*]] = arith.constant dense<1.000000e+00> : tensor<f32>

  %0 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %1 = "mhlo.pad"(%arg0, %0) <{edge_padding_high = dense<0> : tensor<3xi64>, edge_padding_low = dense<1> : tensor<3xi64>, interior_padding = dense<0> : tensor<3xi64>}> : (tensor<10x10x10xf32>, tensor<f32>) -> tensor<11x11x11xf32>
  %2 = mhlo.constant dense<0.000000e+00> : tensor<f32>

  %strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
  %lhs_dilation = "tf.Const"() {value = dense<[4, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>
  %feature_group_count = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

  %0 = "quantfork.stats"(%arg0) {
    layerStats = dense<[-1.28e-5, 1.27e-5]> : tensor<2xf32>
  } : (tensor<1x5x5x2xf32>) -> tensor<1x5x5x2xf32>
  %w = arith.constant dense<[[[[-1.0, 1.0]]], [[[1.0, 2.0]]], [[[-2.0, 1.0]]]]> : tensor<3x1x1x2xf32>
  %b = arith.constant dense<0.0> : tensor<3xf32>
  %b2 = arith.constant dense<[1.0e-2, 2.1473647e1, -2.1473647e2]> : tensor<3xf32>
  %conv = "tfl.conv_2d"(%0, %w, %b) {

// CHECK-NEXT:     [[PREFIX:%.*]] = "tf.Const"() <{value = dense<"restore_ariables"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
// CHECK-NEXT:     [[SLICE:%.*]] = "tf.Const"() <{value = dense<""> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>
// CHECK-NEXT:     [[NAME:%.*]] = "tf.Const"() <{value = dense<"y"> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>

    %cst_3 = "tf.Const"() {value = dense<0.0235294122> : tensor<f32>} : () -> tensor<f32>
    %cst_4 = "tf.Const"() {value = dense<0.0751230493> : tensor<1xf32>} : () -> tensor<1xf32>
    %cst_5 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_6 = "tf.Const"() {value = dense<0.0441384129> : tensor<f32>} : () -> tensor<f32>

// CHECK-DAG: %[[CONST_0:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = stablehlo.constant dense<6.000000e+00> : tensor<f32>
// CHECK-DAG: %[[CONST_2:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK-DAG: %[[CONST_3:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2x3x3x2xf32>

      %outputs_6, %control_7 = tf_executor.island wraps "tf.Const"() {_post_device_rewrite = true, device = "", value = dense<> : tensor<0xi32>} : () -> tensor<0xi32>
      %outputs_8, %control_9 = tf_executor.island wraps "tf.Const"() {_post_device_rewrite = true, device = "", value = dense<> : tensor<0xi32>} : () -> tensor<0xi32>

// PerTensor-DAG: %[[q_w1:.*]] = "tf.Const"() <{value = dense<127> : tensor<2x3x3x1xi8>}>
// PerTensor-DAG: %[[q_w2:.*]] = "tf.Const"() <{value = dense<127> : tensor<2x3x3x2xi8>}> : () -> tensor<2x3x3x2xi8>
// PerTensor-DAG: %[[scale:.*]] = "tf.Const"() <{value = dense<0.0236220472> : tensor<f32>}> : () -> tensor<f32>
// PerTensor-DAG: %[[zp:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>

  %in = arith.constant dense<0.0> : tensor<3x3x48x1xf32>
  %min = arith.constant dense<0.0> : tensor<48xf32>
  %max = arith.constant dense<255.0> : tensor<48xf32>
  %mini = "tf.Identity"(%min) : (tensor<48xf32>) -> tensor<48xf32>
  %maxi = "tf.Identity"(%max) : (tensor<48xf32>) -> tensor<48xf32>
  %s1 = arith.constant dense<[3, 3, 48]> : tensor<3xi32>
  %s2 = arith.constant dense<[3, 3, 48, 1]> : tensor<4xi32>