%out, %c1 = tf_executor.island wraps "tf.opA"(%ri) {_tpu_replicate="cluster"}: (tensor<i32>) -> tensor<i32>
    // expected-warning @+1 {{TF2XLA TPU bridge input check: cluster op = tf.opA with cluster = cluster has successor as non cluster op tf.opB}}
    %out2, %c2 = tf_executor.island wraps "tf.opB"(%out) : (tensor<i32>) -> tensor<i32>

// CHECK:       [[RES_1:%.*]] = "tf.Add"([[CST_1]], [[CST_0]])
// CHECK:       [[RES_2:%.*]] = "tf.Mul"([[RES_1]], [[CST_1]])
// CHECK:       tf.WhileRegion
// CHECK:       ^bb0
// CHECK:       tf.OpA
// CHECK:       ^bb0([[ARG_2:%[a-zA-Z0-9_]+]]
// CHECK-SAME:  [[ARG_3:%[a-zA-Z0-9_]+]]: tensor<i32>)
// CHECK-NEXT:  [[RES_3:%.*]] = "tf.AddV2"([[ARG_2]], [[RES_1]])
// CHECK-NEXT:  [[RES_4:%.*]] = "tf.Div"([[ARG_3]], [[RES_2]])

	if err != nil {
		return false, err
	}

	// Handle large OPA responses when OPA URL is of
	// form http://localhost:8181/v1/data/httpapi/authz
	type opaResultAllow struct {
		Result struct {
			Allow bool `json:"allow"`
		} `json:"result"`
	}

	// Handle simpler OPA responses when OPA URL is of
	// form http://localhost:8181/v1/data/httpapi/authz/allow
	type opaResult struct {

  // CHECK-SAME: n = 2
  // CHECK:   tf_device.return
}

// -----

// CHECK-LABEL: func @replicate_with_multiple_operands
func.func @replicate_with_multiple_operands() {
  %0 = "tf.opA"() : () -> tensor<*xi1>
  %1 = "tf.opB"() : () -> tensor<*xi1>
  %2 = "tf.opC"() : () -> tensor<*xi1>
  %3 = "tf.opD"() : () -> tensor<*xi32>
  %4 = "tf.opE"() : () -> tensor<*xi32>
  %5 = "tf.opF"() : () -> tensor<*xi32>

		cfg.Compression.MimeTypes = strings.Split(compress.DefaultMimeTypes, config.ValueSeparator)
	case "30":
		// V30 -> V31
		cfg.OpenID = openid.Config{}
		cfg.Policy.OPA = opa.Args{
			URL:       &xnet.URL{},
			AuthToken: "",
		}
	case "31":
		// V31 -> V32
		cfg.Notify.NSQ = make(map[string]target.NSQArgs)
		cfg.Notify.NSQ["1"] = target.NSQArgs{}
	}

  // CHECK-NEXT:     "tf.OpA"(%[[REDUCED]]) : (tensor<f32>) -> ()
  tf_device.replicate {n = 4 : i32} {
    "tf_device.cluster"() ({
      "tf.OpA"(%arg0) : (tensor<f32>) -> ()
      tf_device.return

    during TPURewritePass.

    For example, the tf.OpA with the `mini_batch_splits` attribute will be
    moved after _TPUCompileMlir and the first input will use the
    _TPUCompileMlir program output:

    ```mlir
    "tf_device.launch"() ({
     %cst_0 = "tf.Const"() {value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
     "tf.OpA"(%cst_0) { mini_batch_splits = ""} : (tensor<1x!tf_type.string>) -> ()

//
// tf_executor.island wraps "tf.OpA"
// tf_executor.island {
//  "tf_device.replicate" {n = 2} {
//    "tf.OpB"
//    "tf_device.parallel_execute"() ({
//      "tf.OpC"
//    }, {
//      "tf.OpD"
//    })
//  }
//
// The above IR will be flattened after `ReplicateToIslandPass` and
// `ParallelExecuteToIslandsPass` as follows:
//
// tf_executor.island wraps "tf.OpA"

    For example, the following:

    ```mlir
    func @single_op_launch() {
      %a = "tf.opA"() {device = "CPU:0"} : () -> tensor<i1>
      return %a
    }
    ```

    will be transformed into:

    ```mlir
    func @single_op_launch() {
      %1 = tf_device.launch() ( {
        %a = "tf.opA"() : () -> tensor<i1>
        tf_device.return %a
      }) {device = "CPU:0"} : () -> tensor<i1>

  //
  // Two ops with the same groups and different branches are considered
  // parallel so are not made dependent. For example if `OpA` has parallel_ids
  //   `{group0:branch0, group1:branch0}`
  // and `OpB` has parallel_ids
  //   `{group0:branch1, graph1:branch0}`
  // then `OpA` and `OpB` are executed in parallel because `group0` is common
  // with a different branch.
  //