$2 == "PENDIN" ||
		$2 == "TOSTOP" ||
		$2 ~ /^PAR/ ||
		$2 ~ /^SIG[^_]/ ||
		$2 ~ /^O[CNPFP][A-Z]+[^_][A-Z]+$/ ||
		$2 ~ /^IN_/ ||
		$2 ~ /^LOCK_(SH|EX|NB|UN)$/ ||
		$2 ~ /^(AF|SOCK|SO|SOL|IPPROTO|IP|IPV6|ICMP6|TCP|EVFILT|NOTE|EV|SHUT|PROT|MAP|PACKET|MSG|SCM|MCL|DT|MADV|PR)_/ ||
		$2 == "ICMPV6_FILTER" ||
		$2 == "SOMAXCONN" ||
		$2 == "NAME_MAX" ||
		$2 == "IFNAMSIZ" ||
		$2 ~ /^CTL_(MAXNAME|NET|QUERY)$/ ||

	SIO_KEEPALIVE_VALS                 = IOC_IN | IOC_VENDOR | 4
	SIO_UDP_CONNRESET                  = IOC_IN | IOC_VENDOR | 12

	// cf. https://learn.microsoft.com/en-US/troubleshoot/windows/win32/header-library-requirement-socket-ipproto-ip

	IP_TOS             = 0x3
	IP_TTL             = 0x4
	IP_MULTICAST_IF    = 0x9
	IP_MULTICAST_TTL   = 0xa
	IP_MULTICAST_LOOP  = 0xb
	IP_ADD_MEMBERSHIP  = 0xc
	IP_DROP_MEMBERSHIP = 0xd

  // going forward. So, remove parsing binary sharding.
  absl::StatusOr<xla::HloSharding> sharding_hlo = xla::ParseSharding(shard_str);
  if (sharding_hlo.ok()) {
    sharding = sharding_hlo->ToProto();
    return mlir::success();
  }
  if (report_error)
    llvm::errs() << std::string(sharding_hlo.status().message()) << "\n";
  return mlir::failure();
}

    TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelContext*>(ctx);
  auto fdef_lib =
      cc_ctx->function_library()->GetFunctionLibraryDefinition()->ToProto();
  auto cc_status =
      tensorflow::MessageToBuffer(fdef_lib, serialized_function_def_library);
  tensorflow::Set_TF_Status_from_Status(status, cc_status);
}

		$2 ~ /^KCM/ ||
		$2 ~ /^LANDLOCK_/ ||
		$2 ~ /^LOCK_(SH|EX|NB|UN)$/ ||
		$2 ~ /^LO_(KEY|NAME)_SIZE$/ ||
		$2 ~ /^LOOP_(CLR|CTL|GET|SET)_/ ||
		$2 == "LOOP_CONFIGURE" ||
		$2 ~ /^(AF|SOCK|SO|SOL|IPPROTO|IP|IPV6|TCP|MCAST|EVFILT|NOTE|SHUT|PROT|MAP|MREMAP|MFD|T?PACKET|MSG|SCM|MCL|DT|MADV|PR|LOCAL|TCPOPT|UDP)_/ ||
		$2 ~ /^NFC_(GENL|PROTO|COMM|RF|SE|DIRECTION|LLCP|SOCKPROTO)_/ ||
		$2 ~ /^NFC_.*_(MAX)?SIZE$/ ||

}

TEST(CAPI, TestFromProto) {
  Tensor t_cc(DT_FLOAT, TensorShape({2, 3}));
  t_cc.flat<float>().setConstant(1.0);
  tensorflow::TensorProto t_proto;
  t_cc.AsProtoField(&t_proto);

  TF_Buffer* t_buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(t_proto, t_buffer));

  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(

  if (!s.ok()) return s;

  GraphDef graphdef_out;
  graph_out->ToGraphDef(&graphdef_out);
  graphdef->Swap(&graphdef_out);

  *library = lib_def->ToProto();
  // Remove "_xla_inferred_shapes" attr. They are added by
  // `PerformStaticShapeInferenceBeforeEncapsulation`.
  for (FunctionDef& fdef : *library->mutable_function()) {

	if err := res.Body.Close(); err != nil {
		t.Errorf("Close = %v", err)
	}

	if err := <-writeErr; err == nil {
		t.Errorf("expected non-nil write error")
	}
}

type fooProto struct{}

func (fooProto) RoundTrip(req *Request) (*Response, error) {
	res := &Response{
		Status:     "200 OK",
		StatusCode: 200,
		Header:     make(Header),

        if (node != nullptr && node->IsOp()) {
          NodeDef* const node_def = graph_def.add_node();
          *node_def = node->def();
        }
      }
      *graph_def.mutable_library() = graph.flib_def().ToProto();
      if (flags::Global().more_stack_traces.value()) {
        *graph_def.mutable_debug_info() = graph.BuildDebugInfo();
      }
      session->graph->mu.unlock();

      const auto* flib_crash_handle = crash_analysis::ReportProtoDataOnCrash(
          absl::StrCat(current_file_prefix, "_mlir_import_flib.pbtxt"),
          reachable_flib.ToProto());
      cleanup = [=]() {
        crash_analysis::RemoveReportData(graph_crash_handle);
        crash_analysis::RemoveReportData(flib_crash_handle);
      };
    }