}

// -----

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

func.func @testClusterFuncOpFails(%arg0: tensor<i32>) -> tensor<i32> {
   // expected-error@below {{failed TF functional to executor validation, op tf_device.cluster_func is not allowed}}
  %cluster = "tf_device.cluster_func"(%arg0) {func = @_func} : (tensor<i32>) -> tensor<i32>
 func.return %cluster : tensor<i32>
}

// -----

    %cst = "tf.Const"() {value = dense<0> : tensor<i64>} : () -> tensor<i64>
    %cst_0 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %cst_1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

// CHECK-DAG: %[[CST_0:.*]] = "tf.Const"() <{value = dense<-43> : tensor<i32>}> : () -> tensor<i32>
// CHECK-DAG: %[[CST_1:.*]] = "tf.Const"() <{value = dense<0.0117647061> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[CST_2:.*]] = "tf.Const"() <{value = dense<1.38408304E-5> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[CST_3:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main(%arg0 : tensor<1xf32>) -> tensor<1xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

      %2:4 = tf_device.replicate([%arg0, %arg1] as %arg2: tensor<i1>) {n = 2 : i32} {
        %3 = "tf.opA"(%arg2) : (tensor<i1>) -> tensor<f32>
        %4 = "tf.opB"(%arg2) : (tensor<i1>) -> tensor<i32>
        tf_device.return %3, %4 : tensor<f32>, tensor<i32>
      }
      tf_executor.yield %2#0, %2#1, %2#2, %2#3 : tensor<f32>, tensor<f32>, tensor<i32>, tensor<i32>
    }

//CHECK-DAG:    %[[c1:.+]] = stablehlo.constant dense<1> : tensor<i32>
//CHECK-DAG:    %[[c2:.+]] = stablehlo.constant dense<0> : tensor<i32>
//CHECK-NEXT:   %[[c3:.+]] = stablehlo.dynamic_update_slice %arg0, %[[c0]], %[[c1]], %[[c2]], %[[c2]] : (tensor<2x1x2xf32>, tensor<1x1x2xf32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<2x1x2xf32>
//CHECK-NEXT:   return %[[c3]] : tensor<2x1x2xf32>

func.func @decompose_tf_cast(%arg0: tensor<f32>) -> tensor<i32> {
  %0 = "tfr.cast"(%arg0) : (tensor<f32>) -> !tfr.tensor
  %t = tfr.constant i32 -> !tfr.attr
  %concat = tfr.call @tf__risc_cast(%0, %t) : (!tfr.tensor, !tfr.attr) -> !tfr.tensor
  %4 = "tfr.cast"(%concat) : (!tfr.tensor) -> tensor<i32>
  func.return %4 : tensor<i32>

// CHECK: %[[tfcast:.*]] = "tf.RiscCast"(%arg0) {K = i32} : (tensor<f32>) -> tensor<*xi32>

  func.func @main() -> () {
    %0 = "tf.Const"() <{value = dense<-1> : tensor<3360x8xi32>}> : () -> tensor<3360x8xi32>
    %cst_33 = "tf.Const"() <{value = dense<[1120, -1]> : tensor<2xi32>}> : () -> tensor<2xi32>
    %cst_34 = "tf.Const"() <{value = dense<[3, 1120, -1]> : tensor<3xi32>}> : () -> tensor<3xi32>
    %cst_63 = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>

  func.return %0 : tensor<i32>
}

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