%0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo{{_.*_1}}"
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<1> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo1"

    // The following node would be renamed to bar2
    %3:2 = tf_executor.island wraps "tf.Identity"(%2) {device = "", dtype = "tfdtype$DT_INT32"} : (tensor<10xi32>) -> tensor<10xi32> loc ("bar")

func.func @replicate_arg_shape(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) {
  %0:4 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<*xf32>) {n = 2: i32} {
    %1 = "tf.Shape"(%ri) {device = "", T = "tfdtype$DT_FLOAT", out_type = "tfdtype$DT_INT32"} : (tensor<*xf32>) -> tensor<?xi32>
    %2 = "tf.opA"(%1, %ri) : (tensor<?xi32>, tensor<*xf32>) -> tensor<*xi32>
    tf_device.return %1, %2 : tensor<?xi32>, tensor<*xi32>
  }
  func.return
}

    %3 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    "tf.AssignAddVariableOp"(%0, %2) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
    "tf.AssignAddVariableOp"(%1, %3) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2xi32>>>, tensor<i32>) -> ()
    func.return
  }
}

// -----

  // CHECK-NEXT:       tensor_content: "\200\000\000\000\200\000\000\000"
  tf_executor.graph {

// CHECK-NEXT:     buffer: 2
// CHECK-NEXT:   } ],
// CHECK-NEXT:   signature_defs: [  ]
// CHECK-NEXT: }
func.func @main() -> tensor<!tf_type.variant<tensor<2xi32>>> {

attributes {tf.entry_function = {inputs = "input0,input1", outputs = "Add"}} {
  %graph = tf_executor.graph {
    %2:2 = tf_executor.island wraps "tf.Add"(%arg0, %arg1) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %2 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {

// CHECK-NEXT:         }
// CHECK-NEXT:       }
// CHECK:          }
// CHECK-NEXT:   }
func.func @bar() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", name = "tf.Const", value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %1:2 = tf_executor.island wraps "tf.Empty"(%0#0) {dtype = "tfdtype$DT_FLOAT", name = "tf.Empty"} : (tensor<i32>) -> tensor<*xf32>
    tf_executor.fetch

  // ALWAYS-DECOMPOSE-NOT:  "tf.AssignAddVariableOp"
  "tf.AssignAddVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
  // CHECK: tf_device.cluster
  "tf_device.cluster"() ({
    // CHECK-NOT: "tf.AssignAddVariableOp"
    "tf.AssignAddVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
    tf_device.return
  }) : () -> ()

  %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<f32>) -> tensor<0xi32>

  // Result shape need not be static. Folding harness uses TensorFlow constant
  // in that case.
  // CHECK-DAG: "tf.Const"() <{value = dense<[1, 32, 32, 16]> : tensor<4xi32>}> : () -> tensor<?xi32>
  %1 = "tf.Shape"(%arg1) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<1x32x32x16xf32>) -> tensor<?xi32>