%0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
// CHECK: node {
// CHECK-NEXT: name: "const1"
// CHECK-NEXT: op: "Const"
// CHECK: dtype: DT_HALF
// CHECK: half_val: 15360

// 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

// 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>

  %result:2 = "tf.ParseExampleV2"(%serialized, %names, %empty_str_vector, %dense_keys, %empty_str_vector, %dense_default_0, %dense_default_1) {dense_shapes = [#tf_type.shape<>, #tf_type.shape<>], num_sparse = 0 : i64, resultSegmentSizes = array<i32: 0, 0, 0, 2, 0, 0>} : (tensor<32x!tf_type.string>, tensor<32x!tf_type.string>, tensor<0x!tf_type.string>, tensor<2x!tf_type.string>, tensor<0x!tf_type.string>, tensor<?xf32>, tensor<?xf32>)...

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

    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")

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

  %fq = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 5, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<f32>, tensor<f32>) -> tensor<3x3x3x16xf32>
  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
  func.return %rst : tensor<256x8x7x16xf32>

  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:   }
  // 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