code: def onnx_emit(
      original_framework,
      architecture_name,
      architecture_path,
      weight_path,
      test_input_path,
  ):
      from mmdnn.conversion.onnx.onnx_emitter import OnnxEmitter
  
      # IR to code
      converted_file = (
          TestModels.tmpdir
          + original_framework
          + "_onnx_"
          + architecture_name
          + "_converted"
      )
      converted_file = converted_file.replace(".", "_")
      emitter = OnnxEmitter(architecture_path, weight_path)
      emitter.run(
          converted_file + ".py", converted_file + ".npy", "test"
      )
      del emitter
      del OnnxEmitter
  
      # import converted model
      from onnx_tf.backend import prepare
  
      model_converted = imp.load_source(
          "OnnxModel", converted_file + ".py"
      ).KitModel(converted_file + ".npy")
  
      tf_rep = prepare(model_converted)
  
      original_framework = checkfrozen(original_framework)
      func = TestKit.preprocess_func[original_framework][
          architecture_name
      ]
      img = func(test_input_path)
      input_data = np.expand_dims(img, 0)
  
      predict = tf_rep.run(input_data)[0]
  
      del prepare
      del model_converted
      del tf_rep
      del sys.modules["OnnxModel"]
  
      os.remove(converted_file + ".py")
      os.remove(converted_file + ".npy")
  
      return predict
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  import os
  import sys
  import imp
  import numpy
  from mmdnn.conversion.examples.imagenet_test import TestKit
  import utils
  from utils import *
  from datetime import datetime
label_desc: Use image node_id 19 for calling the TestModels obj's underlying member method code with example usage: obj.onnx_emit(original_framework, architecture_name, architecture_path, weight_path, test_input_path) and returns: predict