code: def store_eval_model_with_native_udf(eval_model, cfg):
      import copy
  
      sys.path.append(
          os.path.join(
              abs_path, "..", "..", "Extensibility", "ProposalLayer"
          )
      )
      cntk.ops.register_native_user_function(
          "ProposalLayerOp",
          "Cntk.ProposalLayerLib-" + cntk.__version__.rstrip("+"),
          "CreateProposalLayer",
      )
  
      def filter(x):
          return (
              type(x) == cntk.Function
              and x.op_name == "UserFunction"
              and x.name == "ProposalLayer"
          )
  
      def converter(x):
          layer_config = copy.deepcopy(x.attributes)
          return cntk.ops.native_user_function(
              "ProposalLayerOp",
              list(x.inputs),
              layer_config,
              "native_proposal_layer",
          )
  
      model_w_native_udf = cntk.misc.convert(
          eval_model, filter, converter
      )
      model_path = cfg["MODEL_PATH"]
      new_model_path = model_path[:-6] + "_native.model"
      model_w_native_udf.save(new_model_path)
      print(
          "Stored eval model with native UDF to {}".format(
              new_model_path
          )
      )
  
imports: from __future__ import print_function
  import numpy
  import os, sys
  import argparse
  import easydict
  import cntk
  from cntk import Trainer, load_model, Axis, input_variable, parameter, times, combine, softmax, roipooling, plus, element_times, CloneMethod, alias, Communicator, reduce_sum
  from cntk.core import Value
  from cntk.io import MinibatchData
  from cntk.initializer import normal
  from cntk.layers import placeholder, Constant, Sequential
  from cntk.learners import momentum_sgd, learning_parameter_schedule_per_sample, momentum_schedule
  from cntk.logging import log_number_of_parameters, ProgressPrinter
  from cntk.logging.graph import find_by_name, plot
  from cntk.losses import cross_entropy_with_softmax
  from cntk.metrics import classification_error
  from _cntk_py import force_deterministic_algorithms
  from utils.rpn.rpn_helpers import create_rpn, create_proposal_target_layer, create_proposal_layer
  from utils.annotations.annotations_helper import parse_class_map_file
  from utils.od_mb_source import ObjectDetectionMinibatchSource
  from utils.proposal_helpers import ProposalProvider
  from FastRCNN.FastRCNN_train import clone_model, clone_conv_layers, create_fast_rcnn_predictor, create_detection_losses
label_desc: Use image node_id 5 for calling a global function with example usage: store_eval_model_with_native_udf(eval_model, cfg) and returns: unknown, cntk