// CHECK-LABEL: main
func.func @main(%arg0: tensor<f32>, %arg1: tensor<f32>) -> (tensor<f32>) {
  // CHECK: %[[INPUT0:.*]] = "tf.Placeholder.input"
  %0 = "tf.Placeholder.input"(%arg0) : (tensor<f32>) -> tensor<f32>
  // CHECK: %[[INPUT1:.*]] = "tf.Placeholder.input"
  %1 = "tf.Placeholder.input"(%arg1) : (tensor<f32>) -> tensor<f32>
  %2 = arith.constant dense<true> : tensor<i1>

  %0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %1 = "tf.AddV2"(%0, %0) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %2 = "tf.AddV2"(%0, %0) {device = "/job:worker/replica:0/task:0/device:CPU:1"} : (tensor<f32>, tensor<f32>) -> tensor<f32>
  func.return
}

// CHECK: func @no_change_test
func.func @no_change_test() -> ()  {

      "tf.Yield"(%recv) : (tensor<f32>) -> ()
    }, {
      %add = "tf.Add"(%arg1, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
      "tf.Yield"(%add) : (tensor<f32>) -> ()
    }) { is_stateless = true}: (tensor<i1>) -> tensor<f32>
  // CHECK: _xla_token_input_nodes = ["_xla_token_arg_node"]

  func.return %0, %1 : tensor<f32>, tensor<f32>
}


// Next four functions are used to verify handling of a call chain.

// CHECK: %[[XLA_CALL_MODULE:.+]] = "tf.XlaCallModule"(%[[CUSTOM_AGGREGATOR_0]], %[[CST]])

}

// CHECK-LABEL: not_fold_cast_fft_to_rfft
func.func @not_fold_cast_fft_to_rfft(%arg0: tensor<10x20x30xcomplex<f64>>) -> tensor<10x20x30xcomplex<f32>> {
  %0 = "tf.Cast"(%arg0) : (tensor<10x20x30xcomplex<f64>>) -> tensor<10x20x30xcomplex<f32>>
  %1 = "tf.FFT"(%0) : (tensor<10x20x30xcomplex<f32>>) -> tensor<10x20x30xcomplex<f32>>
  func.return %1: tensor<10x20x30xcomplex<f32>>

  %0 = "tf.Conj"(%arg0) : (tensor<complex<f32>>) -> tensor<complex<f32>>
  %1 = "tf.Conj"(%0) : (tensor<complex<f32>>) -> tensor<complex<f32>>
  // CHECK: return [[ARG0]]
  func.return %1: tensor<complex<f32>>
}

// CHECK-LABEL: func @testTripleConj
// CHECK-SAME:  ([[ARG0:%.+]]: tensor<complex<f32>>)
func.func @testTripleConj(%arg0: tensor<complex<f32>>) -> tensor<complex<f32>> {

// expected-remark@+1 {{HasAtMostTensorArrayEffect: 1}}
func.func @test_tensor_array_effect(%index: tensor<i32>, %size: tensor<i32>, %flow_0: tensor<f32>, %flow_1: tensor<f32>, %handle_0: tensor<2x!tf_type.resource<tensor<?x100xf32>>>, %handle_1: tensor<2x!tf_type.resource<tensor<?x512xf32>>>) -> (tensor<i32>, tensor<i32>, tensor<f32>, tensor<f32>, tensor<2x!tf_type.resource<tensor<?x100xf32>>>, tensor<2x!tf_type.resource<tensor<?x512xf32>>>) {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4 x f32>, tensor<4 x f32>, tensor<4 x f32>, tensor<4 x f32>) -> tensor<4 x f32> {
// CHECK:      {
// CHECK-NEXT:     version: 3,
// CHECK-NEXT:     operator_codes: [ {
// CHECK-NEXT:       deprecated_builtin_code: 27,
// CHECK-NEXT:       version: 1,
// CHECK-NEXT:       builtin_code: SVDF
// CHECK-NEXT:     } ],

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4 x f32>, tensor<4 x f32>, tensor<4 x f32>, tensor<4 x f32>) -> tensor<4 x f32> {
// CHECK:      {
// CHECK-NEXT:     version: 3,
// CHECK-NEXT:     operator_codes: [ {
// CHECK-NEXT:       deprecated_builtin_code: 35,
// CHECK-NEXT:       version: 1,
// CHECK-NEXT:       builtin_code: UNIDIRECTIONAL_SEQUENCE_RNN
// CHECK-NEXT:     } ],

// CHECK:       ENTRY %main.{{[0-9]+}} ([[ARG0:.*]]: f32[2], [[ARG1:.*]]: f32[2]) -> (f32[2]) {
// CHECK-NEXT:    %[[ARG1]] = f32[2]{0} parameter(1)
// CHECK-NEXT:    %[[ARG0]] = f32[2]{0} parameter(0)
// CHECK-NEXT:    ROOT %tuple.{{[0-9]+}} = (f32[2]{0}) tuple(f32[2]{0} %[[ARG0]])
// CHECK-NEXT:  }

// CHECK:       // InputMapping {0, 1}
// CHECK-NEXT:  // XlaInputShape f32[2]
// CHECK-NEXT:  // XlaInputShape f32[2]
// CHECK-NEXT:  // XlaOutputShape (f32[2])