}

func.func @identity(%arg0: tensor<10xi32>, %arg1: tensor<20xi32>, %arg2: tensor<30xi32>) -> (tensor<10xi32>, tensor<20xi32>, tensor<30xi32>, tensor<*xi32>) {
  %0 = "tf.Identity"(%arg0) : (tensor<10xi32>) -> tensor<10xi32>
  %1:2 = "tf.IdentityN"(%arg1,%arg2) : (tensor<20xi32>, tensor<30xi32>) -> (tensor<20xi32>, tensor<30xi32>)
  %2 = "tf.Identity"(%arg0) : (tensor<10xi32>) -> tensor<*xi32>

  func.return %3 : tensor<2xi32>

  // CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<2xi32>
  // CHECK: return %[[CST]] : tensor<2xi32>
}

// CHECK-LABEL: @concat_3_tensors_1_empty
func.func @concat_3_tensors_1_empty() -> tensor<?xi32> {
  %0 = arith.constant dense<1> : tensor<2xi32>
  %1 = arith.constant dense<1> : tensor<2xi32>
  %2 = arith.constant dense<[]> : tensor<0xi32>

func.func @L2NormalizePattern(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  %cst = arith.constant dense<[0]> : tensor<1xi32>
  %0 = "tfl.square"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  %1 = "tfl.sum"(%0, %cst) {keep_dims = false} : (tensor<2xf32>, tensor<1xi32>) -> tensor<f32>
  %2 = "tfl.rsqrt"(%1) : (tensor<f32>) -> tensor<f32>
  %3 = "tfl.mul"(%arg0, %2) {fused_activation_function = "NONE"} : (tensor<2xf32>, tensor<f32>) -> tensor<2xf32>

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

// -----

func.func @unpack(%arg0: tensor<2x3xi32>) -> tensor<2xi32> {
  // expected-error @+2 {{failed to infer returned types}}
  // expected-error @+1 {{op inferred type(s) 'tensor<2xi32>', 'tensor<2xi32>', 'tensor<2xi32>' are incompatible with return type(s) of operation 'tensor<2xi32>', 'tensor<2x1xi32>', 'tensor<2xi32>'}}

  func.return %2, %3, %4, %5: tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>
}

// CHECK-LABEL: func @addNWithZerosInt
func.func @addNWithZerosInt(%arg0: tensor<2xi32>) -> (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) {
  %0 = "tf.Const"() {value = dense<1> : tensor<2xi32>} : () -> tensor<2xi32>
  %1 = "tf.Const"() {value = dense<0> : tensor<2xi32>} : () -> tensor<2xi32>

  %261 = "tf.Shape"(%254) {device = ""} : (tensor<?x?xi64>) -> tensor<2xi32>
  %262 = "tf.StridedSlice"(%261, %6, %15, %16) {begin_mask = 0 : i64, device = "", ellipsis_mask = 0 : i64, end_mask = 1 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<0xi32>

  %outputs_0 = "tf.Const"() {value = dense<224> : tensor<2xi32>} : () -> tensor<2xi32>
  %outputs_2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %outputs_4 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %outputs_6 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>

// CHECK-DAG:       [[VAL_33:%.*]] = arith.constant dense<[-1, 0, 0]> : tensor<3xi32>
// CHECK-DAG:       [[VAL_34:%.*]] = arith.constant dense<0> : tensor<3xi32>

// 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>

  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>