%4 = "tf.MatMul"(%arg0, %3) {device = "", transpose_a = false, transpose_b = false} : (tensor<2x3xf32>, tensor<3x4xf32>) -> tensor<2x4xf32>
  %5 = "tf.Identity"(%4) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>
  %6 = "tf.Identity"(%5) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>
  func.return %6 : tensor<2x4xf32>

  %5 = "tfl.pseudo_const"() {value = dense<[[-0.435141891, -0.940576493, 1.30446923, -1.02953017], [0.684501767, 0.363370508, -2.29151702, 2.41928673]]> : tensor<2x4xf32>} : () -> tensor<2x4xf32>

// CHECK-SAME: (%[[ARG0:.*]]: tensor<3x4xf32>)
func.func @expm1(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
  %0 = "tf.Expm1"(%arg0) : (tensor<3x4xf32>) -> tensor<3x4xf32>
  func.return %0 : tensor<3x4xf32>
  // CHECK: %[[ONE:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
  // CHECK: %[[EXP:.*]] = "tf.Exp"(%[[ARG0]]) : (tensor<3x4xf32>) -> tensor<3x4xf32>

  %4 = "tfl.concatenation"(%1, %3) {axis = -1 : i32, fused_activation_function = "NONE"} : (tensor<2x1xf32>, tensor<2x3xf32>) -> tensor<2x4xf32>
  %5 = "tfl.add"(%4, %arg2) {fused_activation_function = "NONE"} : (tensor<2x4xf32>, tensor<2x4xf32>) -> tensor<2x4xf32>
  func.return %5: tensor<2x4xf32>

// MixedPrecision-NEXT: %[[q:.*]] = "tfl.quantize"(%arg0)
// MixedPrecision-NEXT: %[[dq:.*]] = "tfl.dequantize"(%[[q]])

    %13 = "tf.Mul"(%12, %cst_0) {device = ""} : (tensor<2x4xf32>, tensor<f32>) -> tensor<2x4xf32>
    %14 = "tf.Relu"(%13) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>
    %15 = "tf.Minimum"(%14, %cst) {device = ""} : (tensor<2x4xf32>, tensor<f32>) -> tensor<2x4xf32>
    %16 = "tf.Identity"(%15) {device = ""} : (tensor<2x4xf32>) -> tensor<2x4xf32>

    %8 = "tf.ConcatV2"(%2, %6, %cst_1) : (tensor<2x2xf32>, tensor<2x2xf32>, tensor<i32>) -> tensor<2x4xf32>
    return %8 : tensor<2x4xf32>
  }
  func.func private @composite_matmul_fn_2(%arg0: tensor<2x2xf32>, %arg1: tensor<2x2xf32>) -> tensor<2x2xf32> attributes {tf_quant.composite_function} {