%1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_BOOL", value = dense<true> : tensor<i1>} : () -> tensor<i1> loc("Cond")
    %2:3 = tf_executor.Switch %0#0, %1#0 : (tensor<i32>, tensor<i1>) -> (tensor<i32>, tensor<i32>, !tf_executor.control) {device = "", T = "tfdtype$DT_INT32"} loc("switch")

      %outputs_5, %control_6 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_3) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input0"} : (tensor<0xf32>) -> tensor<0xf32>

wraps "tf.ParseExampleV2"(%arg0, %outputs_4, %outputs_8, %outputs_2, %outputs_6, %outputs, %outputs_0) {Tdense = ["tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], dense_shapes = [#tf_type.shape<>, #tf_type.shape<>], device = "", num_sparse = 2 : i64, ragged_split_types = [], ragged_value_types = [], resultSegmentSizes = array<i32: 2, 2, 2, 2, 0, 0>, sparse_types = ["tfdtype$DT_STRING", "tfdtype$DT_INT64"]} : (tensor<32x!tf_type.string>, tensor<0x!tf_type.string>, tensor<2x!tf_type.string>, tensor<2x!tf_type.string>,...

// CHECK: shape
// CHECK: dim
// CHECK: size: 10

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>

    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<17> : tensor<i32>} : () -> tensor<i32> loc("x")
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_BOOL", value = dense<true> : tensor<i1>} : () -> tensor<i1> loc("predicate")
    %2:3 = tf_executor.Switch %0#0, %1#0 : (tensor<i32>, tensor<i1>) -> (tensor<i32>, tensor<i32>, !tf_executor.control) {device = "", T = "tfdtype$DT_INT32"} loc("switch")

// CHECK-NEXT:         attr {
// CHECK:              key: "dtype"
// CHECK-NEXT:         value {
// CHECK-NEXT:           type: DT_FLOAT
// CHECK-NEXT:         }
// CHECK-NEXT:       }
// 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>

    %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
  }
}

// -----

  using type = float;
  static constexpr TF_DataType kDType = TF_FLOAT;
};

struct DoubleType {
  using type = double;
  static constexpr TF_DataType kDType = TF_DOUBLE;
};

struct Int32Type {
  using type = int32_t;
  static constexpr TF_DataType kDType = TF_INT32;
};

struct UINT8Type {
  using type = uint8_t;
  static constexpr TF_DataType kDType = TF_UINT8;
};

struct INT8Type {

  %0:6 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<*x!tf_type.resource>) {n = 2: i32} {
    %1 = "tf.ReadVariableOp"(%ri) {dtype = "tfdtype$DT_FLOAT"} : (tensor<*x!tf_type.resource>) -> tensor<*xf32>
    %2 = "tf.Shape"(%1) {device = "", T = "tfdtype$DT_FLOAT", out_type = "tfdtype$DT_INT32"} : (tensor<*xf32>) -> tensor<?xi32>
    %3 = "tf.opA"(%1, %2, %ri) : (tensor<*xf32>, tensor<?xi32>, tensor<*x!tf_type.resource>) -> tensor<*xi32>

    // 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