op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "spc1"
  op: "StatefulPartitionedCall"
  input: "input0"
  input: "args_0"
  attr {
    key: "Tin"
    value {
      list {
        type: DT_FLOAT
        type: DT_RESOURCE
      }
    }
  }
  attr {
    key: "Tout"

func.func @convert_xla_must_compile(%arg0: tensor<i32>) -> tensor<i32> {
  // CHECK: "tf.StatefulPartitionedCall"(%arg0) <{config = "", config_proto = "", executor_type = "", f = @stateful_pcall_func}> {_xla_compile_device_type = "CPU", device = "/device:CPU:0"} : (tensor<i32>) -> tensor<i32>

  "tf.PartitionedCall"() {config = "",  config_proto = "", executor_type = "", f = @g} : () -> ()
  // CHECK: "tf.StatefulPartitionedCall"() <{config = "",  config_proto = "", executor_type = "", f = @[[NEWG:.+]]}> : () -> ()
  "tf.StatefulPartitionedCall"() {config = "",  config_proto = "", executor_type = "", f = @g} : () -> ()
  func.return
}

// CHECK: func.func @g()

    (tensor<1x10xf32> {tf_saved_model.index_path = ["output_0"]}, tensor<1x10xi64> {tf_saved_model.index_path = ["output_1"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_x:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %handle_0 = "tf.VarHandleOp"() {container="c", shared_name="a"} : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>

  # CHECK-SAME:   tensor<f32> {tf_saved_model.index_path = [1]})
  # CHECK-SAME: attributes{{.*}}tf_saved_model.exported_names = ["callee"]
  # CHECK:        "tf.StatefulPartitionedCall"{{.*}}f = @[[CALLEE_INTERNAL:[a-zA-Z_0-9]+]]
  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<f32> {tf._user_specified_name = "x", tf_saved_model.index_path = [0]},

func.func @nested_entry_functions() attributes {tf.entry_function = {}} {
  tf_executor.graph {
     %control = tf_executor.island wraps "tf.StatefulPartitionedCall"() {config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @func} : () -> ()
     tf_executor.fetch
  }
  func.return
}

func.func @func() attributes {tf.entry_function = {}} {

  func.func @main(%arg0: tensor<?x!tf_type.string> {tf_saved_model.index_path = ["serving_default_input_vocabs:0"]}) -> (tensor<*xi64> {tf_saved_model.index_path = ["StatefulPartitionedCall:0"]})
      attributes {tf.entry_function = {inputs = "serving_default_input_vocabs:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["main"]} {
    %0 = tf_executor.graph {

  std::vector<NodeBuilder::NodeOut> inputs({NodeBuilder::NodeOut(a.node())});

  Node* call;
  NameAttrList f_name_attr;
  f_name_attr.set_name(fd.signature().name());
  TF_ASSERT_OK(
      NodeBuilder("B", "StatefulPartitionedCall", &root.graph()->flib_def())
          .Input(inputs)
          .Attr("Tin", {DT_RESOURCE})
          .Attr("Tout", {DT_RESOURCE})
          .Attr("f", f_name_attr)
          .Finalize(root.graph(), &call));

      ::llvm::cl::desc("Skip tf.ResizeBilinear and tf.ResizeNearestNeighbor")};

  Option<bool> skip_partitioned_calls_{
      *this, "skip-partitioned-calls",
      ::llvm::cl::desc(
          "Skip tf.StatefulPartitionedCall and tf.PartitionedCall")};
};

void TFToMhloPass::runOnOperation() {
  auto func = getOperation();
  MLIRContext *context = func->getContext();

  RewritePatternSet patterns(context);

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v})
  attributes {tf_saved_model.exported_names = ["f"]} {
    "tf.StatefulPartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @f_callee} : (tensor<!tf_type.resource<tensor<f32>>>) -> ()
    func.return
  }