%cst = "tf.Const"() {value = dense<3> : tensor<i32>} : () -> tensor<i32>
  %cst_1 = "tf.Const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %cst_2 = "tf.Const"() {value = dense<[512, 64]> : tensor<2xi32>} : () -> tensor<2xi32>
  %0 = "tf.ExpandDims"(%arg0, %cst) {device = ""} : (tensor<8x64xf32>, tensor<i32>) -> tensor<8x64x1xf32>

  %cst = "tf.Const"() {value = dense<[[[[-8.69931221, 6.44628429], [-9.18393421, 1.53671741], [8.68561744, -3.581774]]]]> : tensor<1x1x3x2xf32>} : () -> tensor<1x1x3x2xf32>
  %cst_0 = "tf.Const"() {value = dense<-1.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %cst_1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>

// CHECK-DAG:       [[VAL_2:%.*]] = "tfl.pseudo_const"{{.*}}dense<0> : tensor<4xi32>
// CHECK-DAG:       [[VAL_3:%.*]] = "tfl.pseudo_const"{{.*}}dense<[0, 0, 0, 256]> : tensor<4xi32>
// CHECK-DAG:       [[VAL_4:%.*]] = "tfl.pseudo_const"{{.*}}dense<[0, 0, 0, 768]> : tensor<4xi32>
// CHECK-DAG:       [[VAL_5:%.*]] = "tfl.pseudo_const"{{.*}}dense<[-1, -1, -1, 256]> : tensor<4xi32>

  // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}>
  // CHECK: %[[TANH:[0-9]*]] = "tf.Tanh"(%arg0) {{.*}} tensor<1x4x4x8xf32>
  // CHECK: %[[RES_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%[[TANH]], %[[RES_PERM]]) {{.*}} tensor<1x8x4x4xf32>
  // CHECK: return %[[RES_TRANSPOSE]]

  %0 = "tf.Const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>

  %1 = "tfl.pseudo_const"() {value = dense<[[0.1]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %2 = "tfl.pseudo_const"() {value = dense<[[0.2]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %3 = "tfl.pseudo_const"() {value = dense<[[0.3]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %4 = "tfl.pseudo_const"() {value = dense<[[0.4]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>

// CHECK-DAG: %[[cst:.*]] = arith.constant dense<[0, 1, 3, 2]> : tensor<4xi32>
// CHECK-DAG: %[[cst_0:.*]] = arith.constant dense<[-1, 36, 1, 32]> : tensor<4xi64>
// CHECK-DAG: %[[cst_1:.*]] = arith.constant dense<[0, 1]> : tensor<2xi32>
// CHECK-DAG: %[[cst_2:.*]] = arith.constant dense<2> : tensor<1xi32>
// CHECK-DAG: %[[cst_3:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>

    %1 = stablehlo.constant dense<1.000000e+03> : tensor<1x1x1x1xf32>  // Input inverse scale.
    %2 = stablehlo.constant dense<-128> : tensor<1x1x1x1xi8>  // Input zero point.
    %3 = stablehlo.constant dense<1> : tensor<3x3x4x4xi8>  // Quantized filter tensor.
    %4 = stablehlo.constant dense<3.000000e+03> : tensor<1x1x1x4xf32>
    %5 = stablehlo.constant dense<4.000000e+03> : tensor<1x1x1x1xf32>  // Output inverse scale.

  %cst = arith.constant dense<0.000000e+00> : tensor<4x2xf32>
  %cst_0 = arith.constant dense<0.000000e+00> : tensor<8xf32>
  %cst_1 = arith.constant dense<[1, 0, 2]> : tensor<3xi32>
  %cst_2 = arith.constant dense<0.000000e+00> : tensor<4x4x2xf32>
  %cst_3 = arith.constant dense<4> : tensor<i32>
  %cst_4 = arith.constant dense<1.000000e+00> : tensor<f32>
  %cst_5 = arith.constant dense<1> : tensor<i32>

  %cst = arith.constant dense<[1, 0]> : tensor<2xi32>
  %cst_0 = arith.constant dense<0> : tensor<i32>
  %cst_1 = arith.constant dense<-1> : tensor<i32>
  %cst_2 = arith.constant dense<1> : tensor<i32>
  %0 = "tf.AddV2"(%arg2, %cst_2) {T = i32, device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>

  %0 = "tfl.pseudo_sparse_const"() {compressed_data = dense<[1.0, 2.0, 0.5, 0.25, -1.0, -2.0, -0.5, -0.25]> : tensor<8xf16>,...