// CHECK: %0 = "tf.PartitionedCall"(%cst, %arg0) <{config = "", config_proto = "", executor_type = "", f = @"sort_map/while_cond/TfMlrtAsyncWhilePredicate"}> : (tensor<i32>, tensor<i32>) -> tensor<i1>

//CHECK-NEXT: return %0 : tensor<i1>
//CHECK-NEXT:}

func.func @dynamic_update_slice(%arg0: tensor<4x4xi64>, %arg1: tensor<2x3xi64>, %arg2: tensor<i64>, %arg3: tensor<i64>) -> tensor<4x4xi64> {

    %cst = "tfl.no_value"() {value = unit} : () -> none

  }

  // CHECK-LABEL: func @_func
  // CHECK-SAME: %[[ARG0:.*]]: tensor<?xi32>,
  // CHECK-SAME: %[[ARG1:.*]]: tensor<?xi32> {mhlo.is_same_data_across_replicas = true}
  // CHECK-SAME: %[[ARG2:.*]]: tensor<?xi32>)
  func.func @_func(%arg0: tensor<?xi32>, %arg1: tensor<?xi32>, %arg2: tensor<?xi32>) -> tensor<?xi32> {
    %0 = "tf._D"(%arg0, %arg1) : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
    func.return %0 : tensor<?xi32>

// -----

// CHECK-LABEL: call_op
func.func @call_op(%arg0: !tfr.tensor<T>, %arg1: !tfr.tensor_list<TL>, %arg2: i32) -> !tfr.tensor<K> {
  %0 = tfr.call @Foo(%arg0, %arg1, %arg2) : (!tfr.tensor<T>, !tfr.tensor_list<TL>, i32) -> !tfr.tensor<K>
  func.return %0 : !tfr.tensor<K>
}

// -----

// CHECK-LABEL: call_op_arg_attr(%arg0: i32) -> !tfr.tensor<K>
func.func @call_op_arg_attr(%arg0: i32) -> !tfr.tensor<K> {

}

// CHECK: func @while_loop_test(%[[ARG_0:.*]]: tensor<i32>, %[[ARG_1:.*]]: tensor<i32>, %arg2: tensor<*xf32>)
func.func @while_loop_test(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<*xf32>) {
  // CHECK-NEXT: tf.WhileRegion
  %0:2 = "tf.WhileRegion"(%arg0, %arg2) ({
  // CHECK-NEXT: bb0(%[[ARG_3:.*]]: tensor<i32>, %[[ARG_4:.*]]: tensor<*xf32>)
  ^bb0(%arg3: tensor<i32>, %arg4: tensor<*xf32>):

func.func @check_enqueue_ops_with_different_attr_disallowed(%arg0: tensor<?x2xi32>, %arg1: tensor<?x2xi32>,
  %arg2 :tensor<?x2xi32>, %arg3: tensor<?xi32>, %arg4: tensor<?xi32>, %arg5: tensor<?xi32>,
  %arg6: tensor<!tf_type.string>, %arg7: tensor<!tf_type.string>, %arg8: tensor<i1>) -> () {
  %0 = "tf.Const"() {value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>

    // CHECK: %[[OUT:.*]], %[[MEAN:.*]], %[[VAR:.*]] = "mhlo.batch_norm_training"({{.*}}, %arg1, %arg2) <{epsilon = 1.000000e-03 : f32, feature_index = 3 : i64}> : (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>)

// CHECK-LABEL: func @FusedBatchNormRelu
func.func @FusedBatchNormRelu(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>) -> (tensor<8x8x8x8xf32>) {
// CHECK-NEXT: %[[Y:[a-z0-9]*]], {{.*}}_FusedBatchNormEx
// CHECK-NEXT: return %[[Y]]

// CHECK-SAME: (%[[ARG0:.*]]: tensor<f32>, %[[ARG1:.*]]: tensor<f32>, %[[ARG2:.*]]: tensor<*xf32>)
func.func @test_supported_lowering_of_tf_while_region(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<*xf32>) -> (tensor<f32>){
  %0:2 = "tf.WhileRegion"(%arg0, %arg2) ({
  ^bb0(%arg3: tensor<f32>, %arg4: tensor<*xf32>):
    %1 = "tf.Identity"(%arg3) : (tensor<f32>) -> tensor<f32>