// CHECK-LABEL: func @constant_in_island
func.func @constant_in_island(%arg0 : tensor<i1>) -> tensor<f32> {
  %0 = tf_executor.graph {
// CHECK: tf_executor.island
// CHECK: tf.Const{{.*}}2.0
    %1:2 = tf_executor.island {
      %0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
      tf_executor.yield %0 : tensor<f32>
    }
// Uses two islands for no other reason than preventing canonicalization from

        tf_device.return %4 : tensor<?xi32>
      }) {device = "/device:test_device:0"} : () -> tensor<?xi32>

      // CHECK: tf_executor.yield %[[LAUNCH_OUTPUT]]
      tf_executor.yield %3 : tensor<?xi32>
    }
    tf_executor.fetch %1#0 : tensor<?xi32>
  }
  func.return %0 : tensor<?xi32>
}

// CHECK: func private @[[LAUNCH]]

// 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
  }
  func.return
}

// CHECK:        function {

    %1:2 = tf_executor.island {
      %3 = "tf.opA"(%arg0) : (tensor<i1>) -> tensor<i1>
      tf_executor.yield %3 : tensor<i1>
    }
    %2:2 = tf_executor.island(%1#1) {
      %4 = "tf.opB"() : () -> tensor<f32>
      tf_executor.yield %4 : tensor<f32>
    }

// CHECK: "tf.opA"
// CHECK: tf_executor.island
// CHECK: "tf.opB"

    tf_executor.fetch %2#0 : tensor<f32>
  }
  func.return %0 : tensor<f32>
}

// CHECK: }
// CHECK: return

func.func @graph_already() {
  tf_executor.graph {
    %control = tf_executor.island {
      tf_executor.yield
    }
    tf_executor.fetch %control : !tf_executor.control
  }
  func.return
}


// CHECK-LABEL: func @graph_already
// CHECK: tf_executor.graph {
// CHECK:   %[[CONTROL:.*]] = tf_executor.island {
// CHECK:     tf_executor.yield

    %0 = tf_executor.graph {
      %1, %c0 = tf_executor.island wraps "tf.Const"() {value = dense<1> : tensor<i1>} : () -> tensor<i1>
      tf_executor.fetch %1 : tensor<i1>
    }
    return %0 : tensor<i1>
  }

  func.func private @while_body(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
    tf_executor.graph {

      %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>
  }

    tf_executor.graph {
      %out, %ctl = tf_executor.island wraps "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
      %out_0, %ctl_0 = tf_executor.island wraps "tf.ReadVariableOp"(%out) : (tensor<!tf_type.resource<tensor<2xf32>>>) -> tensor<2xf32>

  %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>
    }

// CHECK:      {{%.+}}, [[ISLAND_1_CTRL:%.+]] = tf_executor.island {
// CHECK-NEXT:   [[OP_B_OUTPUT:%.+]] = "tf.opB"()
// CHECK:        tf_executor.yield [[OP_B_OUTPUT]] :
// CHECK:      [[OUTPUT_CTRL:%.+]] = tf_executor.island([[ISLAND_0_CTRL]], [[ISLAND_1_CTRL]]) {
// CHECK:      [[FETCH_ISLAND:%.+]] = tf_executor.island([[OUTPUT_CTRL]]) {
// CHECK:      tf_executor.fetch [[FETCH_ISLAND]] : !tf_executor.control