func.func @main(
    %arg0: tensor<10x10xf32>, %scale: tensor<10xf32>, %zp: tensor<10xi32>
  ) -> tensor<10x10xi8> {
  %0 = "tf.UniformQuantize"(%arg0, %scale, %zp) {
    quantization_axis = 1 : i64,
    quantization_min_val = -128 : i64,
    quantization_max_val = 127 : i64
  } : (tensor<10x10xf32>, tensor<10xf32>, tensor<10xi32>) -> tensor<10x10x!tf_type.qint8>

  func.return %r0, %r1 : tensor<1xi32>, tensor<0xi32>
}

// -----

// CHECK-LABEL: @acos
func.func @acos(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  // CHECK-NOT:  tf.Acos
  %0 = "tf.Acos"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}

// -----

// NOFALLBACK-LABEL: @xla_svd

// QDQ-NEXT:  return %[[split]]#0 : tensor<2xf32>
}

// CHECK-LABEL: FoldTranspose
func.func @FoldTranspose(%arg0: tensor<1x10x20x3xf32>) -> tensor<1x20x40x16xf32> {
  %cst = arith.constant dense<[1, 20, 40, 16]> : tensor<4xi32>
  %cst_0 = arith.constant dense<[2, 0, 1, 3]> : tensor<4xi32>

    // CHECK:     tf.BroadcastGradientArgs
    %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) : (tensor<4xi32>, tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>)
    func.return %r0, %r1 : tensor<1xi32>, tensor<0xi32>
  }

  // CHECK-LABEL: @acos
  func.func @acos(%arg0: tensor<2xf32>) -> tensor<2xf32> {
    // CHECK-NOT:  tf.Acos

  func.return %0: tensor<2xi32>
}

// -----

// CHECK-LABEL: @const_dynamic_output
func.func @const_dynamic_output() -> tensor<*xi32> {
  // CHECK: [[CONST:%.*]] = mhlo.constant dense<0> : tensor<2xi32>
  // CHECK: [[CAST:%.*]] = tensor.cast [[CONST]] : tensor<2xi32> to tensor<*xi32>

  %m0 = mhlo.constant dense<0> : tensor<i32>
  %t0 = "tf.Const"() { value = dense<0> : tensor<1xi32> } : () -> tensor<1xi32>
  %m1 = mhlo.constant dense<1> : tensor<i32>
  %t1 = "tf.Const"() { value = dense<1> : tensor<1xi32> } : () -> tensor<1xi32>
  %m2 = mhlo.constant dense<1> : tensor<i32>
  %t2 = "tf.Const"() { value = dense<1> : tensor<1xi32> } : () -> tensor<1xi32>
  %m3 = mhlo.constant dense<1> : tensor<i32>

  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
  %1 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = true
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>
  %2 = "tf.Reshape"(%1, %cst_0) : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32>

  %0 = "tfl.pseudo_const"() { value = dense<[1, 32, 32, 16]> : tensor<4xi32> } : () -> tensor<4xi32>
  %1 = "tfl.pseudo_qconst"() {qtype = tensor<16x!quant.uniform<i32:f32, 1.0>>, value = dense<1> : tensor<16xi32>} : () -> tensor<16x1x1x8x!quant.uniform<i32:f32, 1.0>>
  %2 = "tfl.pseudo_qconst"() {qtype = tensor<16x!quant.uniform<i32:f32, 1.0>>, value = dense<2> : tensor<16xi32>} : () -> tensor<16x!quant.uniform<i32:f32, 1.0>>

  %0 = "tf.Pack"(%arg0, %arg1, %arg2) {axis = 1 : i64} : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2x3xi32>
  func.return %0 : tensor<2x3xi32>

// CHECK-LABEL: pack3Tensors
// CHECK: "tfl.pack"(%arg0, %arg1, %arg2) <{axis = 1 : i32, values_count = 3 : i32}> : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2x3xi32>
}

  func.return %0 : tensor<?x?x?x?xi32>
}

// CHECK-LABEL:   func @add(
// CHECK-SAME:              %[[VAL_0:.*]]: tensor<2xi32>) -> tensor<2xi32> {
// CHECK:           %[[VAL_1:.*]] = "tf.AddV2"(%[[VAL_0]], %[[VAL_0]]) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
// CHECK:           %[[VAL_2:.*]] = "tf.AddV2"(%[[VAL_1]], %[[VAL_0]]) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
// CHECK:           return %[[VAL_2]] : tensor<2xi32>