// CHECK:     return %[[RESULT]]
    func.return %0 : tensor<10xi32>
  }

  // CHECK-LABEL: func @foo
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @foo(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {

    // CHECK: return %[[RESULT]]
    func.return %0 : tensor<10xi32>
  }

  // CHECK-LABEL: func private @_tf_func
  func.func private @_tf_func(%arg0: tensor<?xi32>, %arg1: tensor<*xi32>) {
    // CHECK: tf.StreamResults

    // StreamResults is a pseudo op in this test.

func.func @xla_gather_known_output_shape(%arg0: tensor<5xi32>, %arg1: tensor<1xi32>, %arg2: tensor<1xi32>) -> tensor<i32> {
  // dimension_numbers: {
  //   collapsed_slice_dims: 0
  //   start_index_map: 0
  // }
  %0 = "tf.XlaGather"(%arg0, %arg1, %arg2) {device = "", dimension_numbers = "\12\01\00\1A\01\00", indices_are_sorted = true} : (tensor<5xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>

  %119 = "tf.StridedSlice"(%116, %16, %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<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<0xi32>

  // CHECK: "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<1xi32>) -> tensor<1xi32>
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x!tf_type.qint32>) -> tensor<1xi32>
  func.return %0: tensor<1xi32>
}

// -----

// CHECK-LABEL: func @cast_op_int32_qint32
func.func @cast_op_int32_qint32(%arg0: tensor<1xi32>) -> tensor<1x!tf_type.qint32> {

func.func @main() -> (tensor<1xi32>, tensor<1xi32>) {
  %0, %1 = tf_executor.graph {
    %output_1, %token_1, %control_1 = tf_executor.NextIteration.Source : tensor<1xi32> {T = i32, device = ""}
    %output_2, %token_2, %control_2 = tf_executor.NextIteration.Source : tensor<1xi32> {T = i32, device = ""}
    tf_executor.NextIteration.Sink [%token_1] %output_1 : tensor<1xi32> {T = i32, device = ""}

# NONE-SAME:  control_outputs = ""
# NONE-SAME:  inputs = "input0,input1"
# NONE-SAME:  outputs = "Add"
# NONE:           %[[add:.*]], %[[add_control:.*]] = tf_executor.island wraps "tf.Add"(%[[ARG_0]], %[[ARG_1]])
# NONE:           fetch %[[add]]

# UNKNOWN-LABEL: func @main

      Targs = [tensor<32xf32>, tensor<64xf32>, tensor<16xf32>],
      Tresults = [tensor<32xf32>, tensor<16xf32>]}
      : (tensor<32xf32>, tensor<64xf32>, tensor<16xf32>, tensor<2x!tf_type.string>) -> (tensor<32xf32>, tensor<16xf32>)
    tf_device.return %0#0, %0#1 : tensor<32xf32>, tensor<16xf32>
  }) {device = "/job:localhost/replica:0/task:0/device:TPU:0"} : () -> (tensor<32xf32>, tensor<16xf32>)
  // CHECK-NEXT: tf_device.return
  // CHECK-NEXT: })

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      return %0 : tensor<1xi32>
    }
  })";
  CreateModule(kMlirModuleStr);

  auto result = Run();

  EXPECT_TRUE(result.succeeded());
}

    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %2:2 = tf_executor.island wraps "tf.PlaceholderWithDefault"(%1#0) {type = i32} : (tensor<i32>) -> tensor<*xi32> loc("unranked")