%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>

// 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 = "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>

func.func @QuantizeSlice(tensor<2x3x5x!quant.uniform<u8:f32, 0.1>>, tensor<3xi32>, tensor<3xi32>) -> tensor<?x3x5xf32> {
^bb0(%arg0: tensor<2x3x5x!quant.uniform<u8:f32, 0.1>>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>):
  %0 = "tfl.dequantize"(%arg0) : (tensor<2x3x5x!quant.uniform<u8:f32, 0.1>>) -> tensor<2x3x5xf32>
  %1 = "tfl.slice"(%0, %arg1, %arg2) : (tensor<2x3x5xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<?x3x5xf32>
  func.return %1 : tensor<?x3x5xf32>

    %3 = stablehlo.concatenate %2, %cst_0, %cst_1, %cst_2, dim = 0 : (tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<4xi32>
    %4 = stablehlo.dynamic_broadcast_in_dim %arg2, %3, dims = [0, 1, 2, 3] : (tensor<1x1x1x2xf32>, tensor<4xi32>) -> tensor<?x3x4x2xf32>
    %5 = stablehlo.add %0, %4 : tensor<?x3x4x2xf32>
    return %5 : tensor<?x3x4x2xf32>
  }
}