%control_90 = tf_executor.island(%control_28, %control_87) wraps "tf.NoOp"() {device = ""} : () -> ()
      %outputs_91, %control_92 = tf_executor.island(%control_90) wraps "tf.Identity"(%outputs_88) {device = ""} : (tensor<i1>) -> tensor<*xi1>
      tf_executor.fetch %outputs_91, %control_28, %control_87 : tensor<*xi1>, !tf_executor.control, !tf_executor.control
    }
    return %0 : tensor<*xi1>
  }

  %0:2 = tf_executor.graph {
    %1:2 = tf_executor.island {
      tf_executor.yield %arg1 : tensor<i1>
    }
    %2:2 = tf_executor.island {
      tf_executor.yield %arg0 : tensor<i1>
    }
    %3:2 = tf_executor.island {
      tf_executor.yield %1#0 : tensor<i1>
    }
    %4:2 = tf_executor.island {
      tf_executor.yield %2#0 : tensor<i1>
    }

func.func @multiple_func_body_ops() {
  tf_executor.graph {
    tf_executor.island {
      "tf.NoOp"() : () -> ()
      tf_executor.yield
    }
    tf_executor.fetch
  }
  tf_executor.graph {
    tf_executor.island {
      "tf.NoOp"() : () -> ()
      tf_executor.yield
    }
    tf_executor.fetch
  }
  func.return
}

// -----

  }
  tf_executor.graph {
    tf_executor.fetch
  }
  %1:3 = tf_executor.graph {
    %6:3 = tf_executor.island {
      %7 = "tf.opD"(%arg0) : (tensor<i1>) -> tensor<i1>
      %8 = "tf.opE"(%7) : (tensor<i1>) -> tensor<i1>
      tf_executor.yield %8, %7 : tensor<i1>, tensor<i1>
    }
    tf_executor.fetch %arg0, %6#0, %6#1 : tensor<i1>, tensor<i1>, tensor<i1>
  }
  %9 = tf_executor.graph {

    auto executor_graph =
        rewriter.create<tf_executor::GraphOp>(loc, func_type.getResults());
    rewriter.inlineRegionBefore(graph.getNodes(), executor_graph.getBody(),
                                executor_graph.getBody().end());

    // Add terminator of tf_executor::graph
    rewriter.setInsertionPointToEnd(&executor_graph.getBody().front());
    rewriter.create<tf_executor::FetchOp>(loc);

    // Add terminator of func

func.func @controls_per_replica() {
  tf_executor.graph {
    %1 = tf_executor.ControlTrigger {}
    %2 = tf_executor.ControlTrigger {}
    %3 = tf_executor.island(%1, %2) {
      tf_device.replicate {n = 2 : i32} {
        tf_device.return
      }
      tf_executor.yield
    }
    tf_executor.fetch
  }
  func.return
}

// CHECK: %[[CT_0:.*]] = tf_executor.ControlTrigger

// 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">,

  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"}>

  return llvm::isa<tf_executor::EnterOp, tf_executor::ExitOp,
                   tf_executor::IslandOp, tf_executor::MergeOp,
                   tf_executor::SwitchOp>(op);
}

// Assigns all data results to a specified device.
void PopulateDeviceForOpResults(
    Operation& op, llvm::StringRef device,
    llvm::DenseMap<Value, llvm::StringRef>& value_to_device) {
  Operation* op_to_update = &op;