testing::ZeroArgInputSignature();
  *saved.mutable_output_signature() = testing::ZeroReturnOutputSignature();
  FunctionDef func = FuncDefWithNumInputsOutputs(0, 0);
  *func.mutable_signature()->mutable_name() = func_name;

  {
    std::unique_ptr<TFConcreteFunction> result;
    Status status =
        internal::LoadTFConcreteFunction(saved, &func, {}, context(), &result);

    auto& attr = iter.second;
    if (attr.has_func()) {
      try_use_original_func_name(attr.mutable_func()->mutable_name());
    } else if (attr.has_list()) {
      for (auto& func_attr : *attr.mutable_list()->mutable_func()) {
        try_use_original_func_name(func_attr.mutable_name());
      }
    }
  }
}

Status Exporter::AddInstructionNode(Operation* inst) {
  std::unique_ptr<NodeDef> node_def;

    auto& attr = iter.second;
    if (attr.has_func()) {
      try_use_original_func_name(attr.mutable_func()->mutable_name());
    } else if (attr.has_list()) {
      for (auto& func_attr : *attr.mutable_list()->mutable_func()) {
        try_use_original_func_name(func_attr.mutable_name());
      }
    }
  }
}

Status Exporter::AddInstructionNode(Operation* inst) {
  std::unique_ptr<NodeDef> node_def;

    %control_11 = tf_executor.island wraps "tf.LoadTPUEmbeddingAdagradParameters"(%outputs_2, %outputs_5) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table4"} : (tensor<8xf32>, tensor<8xf32>) -> ()
    tf_executor.fetch
  }
  return
}
// CHECK-LABEL: func @tpu_load_embedding_ops_sink_controls

                              void, FuncT, tensorflow::NodeDef&>>>
void MutateNodeDefs(tensorflow::GraphDef& graph_def, FuncT&& func) {
  for (tensorflow::NodeDef& node_def : *graph_def.mutable_node()) {
    func(node_def);
  }

  for (tensorflow::FunctionDef& function_def :
       *graph_def.mutable_library()->mutable_function()) {
    for (tensorflow::NodeDef& node_def : *function_def.mutable_node_def()) {

    OptionalAttr<F32Attr>:$quantization_config_low,
    OptionalAttr<F32Attr>:$quantization_config_high,
    OptionalAttr<I64Attr>:$quantization_config_num_buckets,
    StrAttr:$table_name
  );

  let results = (outs
    TF_Float32Tensor:$activations
  );
}

def TF_XlaSparseDenseMatmulGradWithSgdAndCsrInputOp : TF_Op<"XlaSparseDenseMatmulGradWithSgdAndCsrInput", [Pure]> {

     %3 = "tf.ReadVariableOp"(%arg3) {device = ""} : (tensor<*x!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>
     "tf.LoadTPUEmbeddingAdagradParameters"(%2, %3) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table2"} : (tensor<8xf32>, tensor<8xf32>) -> ()

     %3 = "tf.ReadVariableOp"(%arg3) {device = ""} : (tensor<*x!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>
     "tf.LoadTPUEmbeddingAdagradParameters"(%2, %3) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table2"} : (tensor<8xf32>, tensor<8xf32>) -> ()

                absl::StrCat(node_def.name(), "@", func_name));
          }
        }
      }
    }
  }

  // Upgrade nodes in the GraphDef.
  for (auto& node_def : *gdef.mutable_node()) {
    const OpDef* op_def = nullptr;
    TF_RETURN_IF_ERROR(flib_def->LookUpOpDef(node_def.op(), &op_def));
    // TODO(b/197144710): improve the shared_name attr, each op may use the
    // shared_name differently.

    }

    @Managed
    abstract static class MutableName implements Named {
        abstract void setName(String name)
    }

    def "Named cannot have setName"() {
        when:
        extract MutableName

        then:
        def e = thrown Exception
        e.message == """Type ${fullyQualifiedNameOf(MutableName)} is not a valid managed type: