"tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<?xf32>, value = dense<1.> : tensor<1xf32> } : () -> ()
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<?xf32>>> {tf_saved_model.bound_input = @v})
  -> (tensor<?xf32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %0 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<?xf32>>>) -> tensor<?xf32>

  // CHECK: func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v})
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v}) -> (tensor<f32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["f"]} {

  // CHECK-LABEL: @read_from_global
  func.func @read_from_global(%arg0: tensor<!tf_type.resource<tensor<10xf32>>> {tf_saved_model.bound_input = @v})
  attributes {tf_saved_model.exported_names = ["read_from_global"]} {
    // CHECK: [[name:%.*]] = "tf.VarHandleOp"
    // CHECK: "tf.ReadVariableOp"([[name]])

# CHECK:      func @[[init]]
# CHECK-SAME: [[ARG0:%.*]]: tensor<!tf_type.string> {tf_saved_model.bound_input = @[[asset0]]}
# CHECK-SAME: [[ARG1:%.*]]: tensor<!tf_type.string> {tf_saved_model.bound_input = @[[asset1]]}
# CHECK-SAME: tf_saved_model.initializer_type = "init_op"
# CHECK-NEXT: [[R0:%.*]] = "tf.HashTableV2"()
# CHECK-SAME: shared_name = "[[hash_table:.*]]"

    Operation *op, unsigned region_index, unsigned arg_index,
    NamedAttribute named_attr) {
  if (named_attr.getName() == "tf_saved_model.bound_input") {
    if (!mlir::isa<FlatSymbolRefAttr>(named_attr.getValue())) {
      return op->emitError() << "'tf_saved_model.bound_input' attribute should "
                                "be a FlatSymbolRefAttr";
    }
    auto symbol_name =

  func.func @serving_default(
      %arg0: tensor<1x3xi32> {tf_saved_model.index_path = ["x"]},
      %arg1: tensor<!tf_type.resource<tensor<3x1xi32>>> {tf_saved_model.bound_input = @y},
      %arg2: tensor<!tf_type.string> {tf_saved_model.bound_input = @z}
    ) -> (tensor<1x1xi32> {tf_saved_model.index_path = ["r"]})
      attributes {
        tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "result:0"},

    module {
      "tf_saved_model.asset"() {filename = "assets/file_0.txt", sym_name = "__tf_saved_model_asset0"} : () -> ()
      func.func @main(%arg_0: tensor<!tf_type.string> {tf_saved_model.bound_input = @__tf_saved_model_asset0}) -> () attributes {tf.entry_function = {inputs = "arg_0:0", outputs = ""}} {
        return
      }
    }
  )mlir");

  FailureOr<SmallVector<AssetFileDef>> asset_file_defs =

  "tf_saved_model.asset"() {filename = "assets/table2.txt", sym_name = "w"} : () -> ()

  // CHECK: func @f()
  func.func @f(%arg0: tensor<!tf_type.string> {tf_saved_model.bound_input = @v}, %arg1: tensor<!tf_type.string> {tf_saved_model.bound_input = @w})
  attributes {tf_saved_model.exported_names = ["f"]} {

  func.func @read(%arg0: tensor<!tf_type.resource<tensor<10xf32>>> {tf_saved_model.bound_input = @v})
  -> (tensor<10xf32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["read"]} {
    // CHECK: %[[v:.*]] = ml_program.global_load @vars.v : tensor<10xf32>

  "tf_saved_model.asset"() {filename = "assets/test1.txt", sym_name = "asset1"} : () -> ()

  // CHECK: func @init()
  func.func @init(%arg0: tensor<!tf_type.string> {tf_saved_model.bound_input = @asset0}, %arg1: tensor<!tf_type.string> {tf_saved_model.bound_input = @asset1}) attributes {tf_saved_model.exported_names = ["init"]} {
    // CHECK-DAG: %[[ASSET0:.*]] = "tf.Const"() <{value = dense<"foo/bar/assets/test0.txt"> : tensor<!tf_type.string>}>