%c = "tf.opC"() : () -> tensor<i1>
      tf_executor.yield %a, %launch#0, %launch#1, %c : tensor<i1>, tensor<f32>, tensor<i32>, tensor<i1>
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK-NOT: tf_executor.island
// CHECK:      %[[A:.*]], {{.*}} = tf_executor.island wraps "tf.opA"
// CHECK:      %[[B:.*]]:2, {{.*}} = tf_executor.island wraps "tf.opB"(%[[A]])
// CHECK-SAME: device = "CPU:0"

func.func @colocate_split_with_pred() {
  // CHECK: Split
  // CHECK-SAME: _class = ["loc:@class"]
  tf_executor.graph {
    %c, %control0 = tf_executor.island wraps "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %a, %control1 = tf_executor.island wraps "tf.A"() {_class = ["loc:@class"]} : () -> (tensor<2xf32>)

    tf_executor.graph {
      %control = tf_executor.island {
        "tf.StatefulPartitionedCall"(%arg0) <{config = "", config_proto = "", executor_type = "", f = @partitioned}> : (tensor<*x!tf_type.resource>) -> ()
        tf_executor.yield
      }
      tf_executor.fetch %control : !tf_executor.control
    }
    return
  }

// This pass forms `tf_executor.island` per region of
// `tf_device.parallel_execute`.
//
// For example, the following:
//
//  %0 = tf_executor.island {
//    tf_executor.yield
//  }
//  %1:2 = tf_executor.island {
//    %2 = "tf.opA"(%arg0) : (tensor<i1>) -> tensor<i1>
//      tf_executor.yield %2 : tensor<i1>
//  }
//  %3:2 = tf_executor.island(%0) {
//    %4 = "tf_device.parallel_execute"() ({

    dependencies.

    Operations in this dialect return a value of type `!tf_executor.control` as
    last returned value (exceptions are `tf_executor.NextIteration.graph`,
    `tf_executor.NextIteration.sink` and `tf_executor.fetch` which don’t return any
    value).
}];

  let cppNamespace = "::mlir::tf_executor";
}

// Control type.
def TfeControlType : Type<CPred<"$_self.isa<ControlType>()">, "control">,

    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", value = dense<"value"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<"value"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>

func.func @bad_tf_device_attr() {
  tf_executor.graph {
    %0 = tf_executor.island {
      // expected-error@+1 {{'tf_device.launch' op inner op has bad 'device' attribute}}
      "tf_device.launch"() ({
        "tf.opA"() {device = 0 : i32} : () -> ()
        tf_device.return
      }) {device = "CPU:0"} : () -> ()
      tf_executor.yield
    }
    tf_executor.fetch
  }
  func.return

func.func @if_then(%arg0: tensor<i1>) -> tensor<i1> {
  // expected-remark@above {{ID: 5}}
  %graph = tf_executor.graph {
  // expected-remark@above {{ID: 3}}
    %island:2 = tf_executor.island {
    // expected-remark@above {{ID: 1}}
      tf_executor.yield %arg0 : tensor<i1>
      // expected-remark@above {{ID: 0}}
    }
    tf_executor.fetch %island#0 : tensor<i1>
    // expected-remark@above {{ID: 2}}
  }

    %0 = tf_executor.graph {
      %outputs_0, %control_0 = tf_executor.island wraps "tf.VarHandleOp"() {container = "", shared_name = ""} : () -> tensor<!tf_type.resource<tensor<501000x128xf32>>>

  func.func @argliveinotherislands(%arg0: tensor<?xi32>) -> tensor<?xi32> {
    %0 = tf_executor.graph {
      // CHECK: %[[OTHER_ISLAND_OUTPUT:[a-z0-9]*]], %{{.*}} = tf_executor.island wraps "tf.D"
      %1:2 = tf_executor.island wraps "tf.D"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>

      %2:2 = tf_executor.island {
        // CHECK: %[[TPU0_OUTPUT:[0-9]*]] = "tf_device.launch"
        // CHECK: <{device = "tpu0"}>