code: def tfe_model_predict(self, tf_model, tf_data, decode_pre):
      model_name = tf_model.name.capitalize()
      batch_size = tf_data.shape[0]
      tfe_data = tfe.define_private_input(
          "prediction-client", lambda: tf_data
      )
  
      Performance.time_log("Model conversion")
      c = convert.Converter(config=config)
      tfe_model = c.convert(
          tf_model,
          list(tf_data.shape),
          model_provider=config.get_player("weights-provider"),
      )
      Performance.time_log("Model conversion")
  
      Performance.time_log(model_name + " Prediction 1st run")
      preds = tfe_model.predict(tfe_data, batch_size=batch_size)
      Performance.time_log(model_name + " Prediction 1st run")
  
      Performance.time_log(model_name + " Prediction 2nd run")
      preds = tfe_model.predict(tfe_data, batch_size=batch_size)
      Performance.time_log(model_name + " Prediction 2nd run")
  
      print("Predicted:", decode_pre(preds, top=10)[0])
  
file_path: tf-encrypted/examples/benchmark/inference/convert.py
imports: import argparse
  import os
  import unittest
  import _pickle
  import numpy
  import tensorflow
  import tf_encrypted
  from tf_encrypted.convert import convert
  from tf_encrypted.performance import Performance
  from tf_encrypted.protocol import ABY3
  from tf_encrypted.protocol import Pond
  from tf_encrypted.protocol import SecureNN
  from tf_encrypted.utils import print_banner
label_desc: Use image node_id 2 for calling the TestConvertInference obj's underlying member method code with example usage: obj.tfe_model_predict(tf_model, tf_data, decode_pre) without return types