code: def convert_onnx_to_model(onnx_input_path):
      """Reimplementation of the TensorFlow-onnx official tutorial convert the onnx file to specific: model
  
      Parameters
      -----------
      onnx_input_path : string
      the path where you save the onnx file.
  
      References
      -----------
      - `onnx-tf exporting tutorial <https://github.com/onnx/tutorials/blob/master/tutorials/OnnxTensorflowExport.ipynb>`__
      """
      model = onnx.load(onnx_input_path)
      tf_rep = prepare(model)
      # Image Path
      img = np.load("./assets/image.npz", allow_pickle=True)
      output = tf_rep.run(img.reshape([1, 784]))
      print("The digit is classified as ", np.argmax(output))
  
file_path: TensorLayer/examples/tutorial_work_with_onnx.py
imports: import time
  import numpy
  import tensorflow
  from tensorflow.python.tools.freeze_graph import freeze_graph
  import onnx
  import tensorlayer
  from onnx_tf.backend import prepare
  from onnx_tf.frontend import tensorflow_graph_to_onnx_model
label_desc: Use image node_id 4 for calling a global function with example usage: convert_onnx_to_model(onnx_input_path) without return types