// 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])

  func.return %arg0 : tensor<!tf_type.resource>
}
func.func @if_else(%arg0: tensor<!tf_type.resource>, %arg1: tensor<!tf_type.resource>) -> tensor<!tf_type.resource> {
  %elem = "tf._SomeOp"() : () -> tensor<f32>
  %push = "tf.StackPushV2"(%arg1, %elem) {swap_memory = false} : (tensor<!tf_type.resource>, tensor<f32>) -> tensor<f32>
  func.return %arg1 : tensor<!tf_type.resource>
}

// -----

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.GuaranteeConst"(%arg1) {T = f32, device = ""} : (tensor<f32>) -> tensor<f32>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.GuaranteeConst"(%arg2) {T = f32, device = ""} : (tensor<f32>) -> tensor<f32>

  // CHECK-DAG: %[[VAL25:.*]] = "tf.Sub"(%[[VAL1]], %[[VAL23]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  // CHECK-DAG: %[[VAL26:.*]] = "tf.Mul"(%[[VAL24]], %[[VAL6]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  // CHECK-DAG: %[[VAL27:.*]] = "tf.Mul"(%[[VAL25]], %[[VAL6]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>

    %3 = "tf.Const"() {value = dense<2.0> : tensor<f32>} : () -> tensor<f32>
    tf_device.return %2, %3 : tensor<i32>, tensor<f32>
  }) {cluster_attr = "cluster_attr"} : () -> (tensor<i32>, tensor<f32>)
  %4 = "tf.Const"() {value = dense<3.0> : tensor<f32>} : () -> tensor<f32>
  %5 = "tf.Add"(%4, %1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %6 = "tf.Const"() {value = dense<4> : tensor<i32>} : () -> tensor<i32>

    %0 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {device = "", max = 2.000000e-01 : f32, min = -1.000000e-01 : f32, narrow_range = false, num_bits = 8 : i64} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>

    %2 = "quantfork.dcast"(%1) : (tensor<4x3x!quant.uniform<i8<-127:127>:f32:1, {5.000000e-03, 5.000000e-03, 5.000000e-03}>>) -> tensor<4x3xf32>
    %3 = "quantfork.qcast"(%arg0) {volatile} : (tensor<1x4xf32>) -> tensor<1x4x!quant.uniform<i8:f32, 6.000000e-03:-128>>
    %4 = "quantfork.dcast"(%3) : (tensor<1x4x!quant.uniform<i8:f32, 6.000000e-03:-128>>) -> tensor<1x4xf32>

      %min_range = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %max_range = "tf.Const"() { value = dense<5.0> : tensor<f32> } : () -> tensor<f32>
      %0 = "tf.Dequantize"(%arg0, %min_range, %max_range) : (tensor<1x!tf_type.qint8>, tensor<f32>, tensor<f32>) -> tensor<1xf32>
      func.return %0 : tensor<1xf32>
    }
  })mlir";

  %2 = "tf.Const"() {value = dense<4.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %3 = tf_executor.graph {
    %res, %ctl = tf_executor.island {
      %3 = "tf_device.cluster"() ({

        // In the device region, check that the 3 constants are materialized and
        // remapped to the uses.
        // CHECK: tf_device.cluster
        // CHECK-DAG: %[[CST2:.*]] = "tf.Const"{{.*}}2.0