module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 486 : i32}} {
  func.func @main() {
    tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", value = dense<1> : tensor<i32>} : () -> tensor<i32>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>

      }, {
        %2 = "tf.opC"() : () -> tensor<i32>
        %3 = "tf.opD"(%2) : (tensor<i32>) -> tensor<i32>
        tf_device.return %3 : tensor<i32>
      }) : () -> (tensor<i1>, tensor<i32>)
      tf_executor.yield %1#0, %1#1 : tensor<i1>, tensor<i32>
    }
    tf_executor.fetch %outputs#0, %outputs#1 : tensor<i1>, tensor<i32>
  }
  return
}

      func.return %0, %1, %2, %3 : tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>
    }
    func.func @while_body_with_cluster_attr(%arg0: tensor<i32>) -> tensor<i32> {
      %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>
      func.return %0 : tensor<i32>
    }

        %4:2 = "tf.TPUReplicatedOutput"(%1) : (tensor<!tf_type.resource<tensor<i32>>>) -> (tensor<!tf_type.resource<tensor<i32>>>, tensor<!tf_type.resource<tensor<i32>>>)
        tf_executor.yield %arg0 : tensor<!tf_type.resource<tensor<i32>>>
      }
      tf_executor.fetch %outputs : tensor<!tf_type.resource<tensor<i32>>>
    }

func.func @then(%x: tensor<i32>, %y: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.AddV2"(%x, %y) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %0 : tensor<i32>
}

func.func @else(%x: tensor<i32>, %y: tensor<i32>) -> (tensor<i32>) {
  %0 = "tf.Const"() {value = dense<1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.AddV2"(%x, %0) : (tensor<i32>, tensor<i32>) -> tensor<i32>

      %a = "tf.opA"(%arg0) : (tensor<i32>) -> tensor<i32>
      %b = "tf.opB"(%a) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %a, %b : tensor<i32>, tensor<i32>
    }
    %output1_0, %output1_1, %control1 = tf_executor.island {
      %c = "tf.opC"(%output0_0) : (tensor<i32>) -> tensor<i32>
      %d = "tf.opD"(%c) : (tensor<i32>) -> tensor<i32>
      tf_executor.yield %c, %d : tensor<i32>, 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> {
  func.return %arg0 : tensor<i32>
}

    func.return %0 : tensor<i32>
  }
  func.func @branch_1(%arg0: tensor<i32>) -> tensor<i32> {
    %0 = "tf.some_op"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i32>) -> tensor<i32>
    func.return %0 : tensor<i32>
  }
  func.func @branch_2(%arg0: tensor<i32>) -> tensor<i32> {

func.func private @then(%x: tensor<i32>, %y: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  func.return %x, %x : tensor<i32>, tensor<i32>
}

func.func private @else(%x: tensor<i32>, %y: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  func.return %y, %y : tensor<i32>, tensor<i32>
}

// CHECK-LABEL: then/tfrt_dedup_results
// CHECK: return {{%.*}} : tensor<i32>

// CHECK-LABEL: else/tfrt_dedup_results

// CHECK-DAG:   [[ITEM_RANK:%.*]] = "tf.Rank"([[ITEM]]) : (tensor<10xf32>) -> tensor<i32>
// CHECK-DAG:   [[ZERO:%cst.*]] = arith.constant dense<0> : tensor<i32>
// CHECK-DAG:   [[VECTOR_RANK:%.*]] = "tf.ExpandDims"([[ITEM_RANK]], [[ZERO]]) : (tensor<i32>, tensor<i32>) -> tensor<1xi32>