%3 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    "tf.AssignAddVariableOp"(%0, %2) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
    "tf.AssignAddVariableOp"(%1, %3) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2xi32>>>, tensor<i32>) -> ()
    func.return
  }
}

// -----

// CHECK: entry function `main` must be present

func.func @const() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

  func.func @main() attributes {tf.entry_function = {inputs = "", outputs = ""}} {
    tf_executor.graph {
      %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
      tf_executor.fetch
    }
    func.return
  }
}

// CHECK-NOT: node

// CHECK: library
// CHECK-NEXT: function

      tf_executor.fetch
    }
    func.return
  }
  func.func @foo() {
    tf_executor.graph {
      %0:2 = tf_executor.island {
        %1 = "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<5> : tensor<i32>} : () -> tensor<i32> loc("control_const")
        tf_executor.yield %1 : tensor<i32>
      }
      // CHECK: control_output: "control_const"
      // CHECK:       control_ret {

  // CHECK-NEXT:   }
  // CHECK:      key: "value"
  // CHECK-NEXT: value {
  // CHECK-NEXT:   s: " 0\n\000\000"
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Placeholder"() {name = "dummy", dtype = "tfdtype$DT_INT32", value = "\200\n\00\00", listvalue = ["\20\0A"]} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

  attributes  {tf.entry_function = {inputs = "args_0,args_1", outputs = "rets_0,rets_1"}} {
    %0:2 = tf_executor.graph {
      %1:3 = tf_executor.island wraps "tf.IdentityN"(%arg0, %arg1) {T = ["tfdtype$DT_FLOAT", "tfdtype$DT_INT32"], device = "", name = "identity"} : (tensor<*xf32>, tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<2x4x6x8xi32>)
      tf_executor.fetch %1#0, %1#1 : tensor<*xf32>, tensor<2x4x6x8xi32>
    }

  // CHECK-NEXT:       tensor_content: "\200\000\000\000\200\000\000\000"
  tf_executor.graph {

    // CHECK-NEXT:      type: DT_FLOAT
    // CHECK-NEXT:    }
    // CHECK-NEXT:  }
    %0:2 = tf_executor.island wraps "tf.Placeholder"() {name = "dummy", dtype = "tfdtype$DT_FLOAT", emptylist = [], typelist = ["tfdtype$DT_INT32", "tfdtype$DT_FLOAT"]} : () -> tensor<*xi32>
    tf_executor.fetch
  }
  func.return

// CHECK-NEXT:   value {
// CHECK-NEXT:     type: DT_INT32{{$}}
// CHECK-NEXT:   }
// CHECK-NEXT: }

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.VariableV2"() {dtype = "tfdtype$DT_INT32", value = dense<2> : tensor<i32>, shape = #tf_type.shape<2>, container = "", shared_name = ""} : () -> tensor<!tf_type.int32ref> loc("Ref_Variable")

// CHECK-NEXT:     buffer: 2
// CHECK-NEXT:   } ],
// CHECK-NEXT:   signature_defs: [  ]
// CHECK-NEXT: }
func.func @main() -> tensor<!tf_type.variant<tensor<2xi32>>> {