// CHECK-SAME: dialect = "bar"
// CHECK: bar.a
// CHECK: bar.b
// CHECK: bar.c
// CHECK: func [[foo]]
// CHECK: foo.someop
// CHECK: call [[bar]]

// -----

func.func @handles_nested_regions(%arg0: f32, %arg1: f32) {
  %0 = "glue.someop"() : () -> f32
  "foo.someop"() ({
    "foo.some_other_op"() ({
      "bar.a"(%arg0) : (f32) -> f32
      "bar.b"(%arg1) : (f32) -> f32

func.func private @handles_multiple_returns(%arg0: f32, %arg1: f32) -> (f32, f32) {
  "SomeOp"() : () -> ()
 ^bb0:
  return %arg0, %arg1 : f32, f32
 ^bb1:
  return %arg1, %arg0 : f32, f32
}

// -----

// CHECK-LABEL: @removes_multiple_returns
func.func private @removes_multiple_returns(%arg0: f32, %arg1: f32) -> (f32, f32) {
  "SomeOp"() : () -> ()
 ^bb0:
  // CHECK: return
  // CHECK-NOT: arg

  %graph:2 = tf_executor.graph {
    %value, %value_control = tf_executor.island wraps "tf.GetKey"() : () -> tensor<f32>
    %ret0, %ret0_control = tf_executor.island wraps "tf.SomeOp"() : () -> !tf_res
    %ret1, %ret1_control = tf_executor.island wraps "tf.SomeOp"() : () -> !tf_res
    %control_unknown = tf_executor.island wraps "tf.UnknownOp"() : () -> ()
    %key, %key_control = tf_executor.island wraps "tf.GetKey"() : () -> !tf_str

// CHECK-LABEL: func @testControlOperands
func.func @testControlOperands() {
  %0:2 = tf_executor.graph {
    %1 = tf_executor.island {
      "tf.someOp"() : () -> ()
      tf_executor.yield
    }
    %2:3 = tf_executor.island(%1) {
      %3:2 = "tf_device.parallel_execute"() ({
        %4 = "tf.opA"() : () -> tensor<i1>
        tf_device.return %4 : tensor<i1>

  func.return
}

// CHECK-LABEL: func @resource
func.func @resource() {
  // CHECK: tf.SomeOp
  // CHECK-SAME: dtype = !corert.resource
  "tf.SomeOp"() {device = "/device:CPU:0", dtype = !tf_type.resource} : () -> ()
  func.return
}

// CHECK-LABEL: func @variant
func.func @variant(%arg: tensor<!tf_type.variant>) {

func.func @simple_independent_chains_while_cond(%arg0: !tf_res, %arg1: !tf_res, %arg2: tensor<f32>, %arg3: tensor<f32>) -> (tensor<i32>) {
  %graph = tf_executor.graph {
    %island, %ctrl = tf_executor.island {
      %pred = "tf.SomeOp"(%arg2) : (tensor<f32>) -> tensor<i32>
      tf_executor.yield %pred : tensor<i32>
    }
    tf_executor.fetch %island : tensor<i32>
  }
  func.return %graph : tensor<i32>
}

  "tf.AssignVariableOp"(%arg0, %device_output) : (!tf_res_i32, tensor<i32>) -> ()
  "tf.SomeOp"(%device_output) : (tensor<i32>) -> ()
  func.return
}

// CHECK-LABEL: func @device_func_1
// CHECK-NOT: tf.aliasing_output
func.func @device_func_1(%arg0: tensor<i32>) -> tensor<i32> {

// CHECK-LABEL: func @testControlOperands
func.func @testControlOperands() {
  %0:2 = tf_executor.graph {
    %1 = tf_executor.island {
      "tf.someOp"() : () -> ()
      tf_executor.yield
    }
    %2:3 = tf_executor.island(%1) {
      %3:2 = "tf_device.parallel_execute"() ({
        %4 = "tf.opA"() : () -> tensor<i1>
        tf_device.return %4 : tensor<i1>

  if (TF_GetCode(status) != TF_OK) {
    fprintf(stderr, "TF_CloseWritableFile failed: %s\n", TF_Message(status));
  }
  TF_StringStreamDone(s);

  TF_KernelBuilder* b =
      TF_NewKernelBuilder("SomeOp", "SomeDevice", &create, &compute, NULL);
  TF_RegisterKernelBuilder("someKernel", b, status);

  TF_DeleteStatus(status);
  return 0;

  // dependent attributes and types with a reverse permutation, and change
  // operation data format to `target_data_format`.
  //
  // Example:
  //   %1 = SomeOp(...)   {data_format = NHWC}
  //   %2 = Transpose(%1) {permutation = NHWC->NCHW}
  //   %3 = Op(%2)        {data_format = NCHW}
  //
  // To bypass %2 we have to change data format to shuffle data format from NCHW