*a = oa
		return nil
	}

	*a = oa
	return nil
}

// Opa - implements opa policy agent calls.
type Opa struct {
	args   Args
	client *http.Client
}

// Enabled returns if opa is enabled.
func Enabled(kvs config.KVS) bool {
	return kvs.Get(URL) != ""
}

// LookupConfig lookup Opa from config, override with any ENVs.

	Compression compress.Config `json:"compress"`

	// OpenID configuration
	OpenID openid.Config `json:"openid"`

	// External policy enforcements.
	Policy struct {
		// OPA configuration.
		OPA opa.Args `json:"opa"`

		// Add new external policy enforcements here.
	} `json:"policy"`

	LDAPServerConfig xldap.LegacyConfig `json:"ldapserverconfig"`

  func.return
}

// CHECK: [[CLUSTER:%.*]] = "tf_device.cluster"() ({
// CHECK:    [[OPA:%.*]] = "tf.opA"() {is_stateless = true} : () -> tensor<i32>
// CHECK:    "tf.opB"() ({
// CHECK:      "tf.opC"([[OPA]])
// CHECK:    tf_device.return [[OPA]]
// CHECK:    "tf.opD"([[CLUSTER]])

// Preceding user is using resource updated by a nested op.

    %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>

	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 {

		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

//
// 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.
  //