tf_device.return %1 : tensor<i32>
  }) {cluster_attr = "cluster_attr"} : () -> tensor<i32>
  %2 = "tf.Const"() {value = dense<5> : tensor<i32>} : () -> tensor<i32>
  %3 = "tf.Add"(%2, %0) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %3 : tensor<i32>
}

// -----

// CHECK-LABEL: func @multi_return_values_in_cluster_op

func.func @listReserveScalarShapeI32(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %0 = "tf.TensorListReserve"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>>
  // CHECK: %0 = "tfl.custom"(%arg0, %arg1) <{custom_code = "TensorListReserve", custom_option = #tfl<const_bytes : "0x02">}> : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>>

    %pred = "tf._SomeOp"(%barg1) : (tensor<i32>) -> tensor<i1>
    "tf.Yield" (%pred) : (tensor<i1>) -> ()
  }, {
  // CHECK: ^bb0(%[[BARG0:.*]]: tensor<f32>, %[[BARG1:.*]]: tensor<i32>):
  ^bb0(%barg0: tensor<f32>, %barg1: tensor<i32>):
    %const1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %sub = "tf.Sub"(%barg1, %const1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %elem = "tf._SomeOp"() : () -> tensor<3xf32>

  func.return %0#2, %0#3, %0#4 : tensor<i32>, tensor<256x256xf32>, tensor<?x256x256xf32>
}

    %2 = "tf.AddV2"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    // CHECK: %[[SUB:.*]] = "tf.Sub"
    %3 = "tf.Sub"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    // CHECK: tf_device.return %arg0, %[[ADD]], %[[MUL]], %[[SUB]]
    tf_device.return %arg0, %2, %0, %3 : tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>
  }) : () -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>)

    %Enter, %ctl_0 = Enter(%Const) [%arg.ctl] name("while/Enter") {T = i32, frame_name = "while/while_context", is_constant = false, parallel_iterations = 10 : i64} : (tensor<i32>) -> (tensor<*xi32>)
    %NextIteration, %ctl_1 = NextIteration(%Const) [%arg.ctl] name("while/NextIteration") {T = i32} : (tensor<i32>) -> (tensor<*xi32>)

    %6 = mhlo.compare  GE, %arg1, %arg3 : (tensor<i32>, tensor<i32>) -> tensor<i1>
    %7 = mhlo.select %6, %arg1, %arg3 : tensor<i1>, tensor<i32>
    %8 = mhlo.compare  EQ, %arg1, %arg3 : (tensor<i32>, tensor<i32>) -> tensor<i1>
    %9 = mhlo.minimum %arg2, %arg4 : tensor<i32>
    %10 = mhlo.select %6, %arg2, %arg4 : tensor<i1>, tensor<i32>
    %11 = mhlo.select %8, %9, %10 : tensor<i1>, tensor<i32>

  %0 = "tfl.conv_2d"(%arg0, %arg1, %arg2) {dilation_h_factor = 2 : i32, dilation_w_factor = 2 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>,...

  %1 = "tf.Const"() {value = dense<5> : tensor<i32>} : () -> tensor<i32>
  %2 = "tf.Add"(%0, %1) : (tensor<i32>, tensor<i32>) -> (tensor<i32>)
  func.return %2 : tensor<i32>
}

func.func @stateful_pcall_func(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}

// -----

                      %n: i32 {tfr.name="N"},
                      %axis: i32 {tfr.name="axis"}) -> !tfr.tensor {
  %index = arith.constant 0 : index
  %cst = arith.constant 1 : i32
  %eq = arith.cmpi eq, %n, %cst : i32
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor
  %temp = tfr.call @tf__expand_dims(%v1, %axis) : (!tfr.tensor, i32) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {