class_docstr: Contains code meant to be executed by some `ModelOwner` Player.
  
  Args:
    player_name: `str`, name of the `tfe.player.Player`, representing the model owner.
    model_name: `str`, name of the model to be trained
    data_name: `str`, name of dataset that model trained on
code: def __init__(self, player_name, model_name, data_name):
      self.player = tfe.get_config().get_player(player_name)
      self.model_name = model_name
      self.data_name = data_name
      self.test_dataset = globals()[self.data_name + "Dataset"](
          batch_size=self.BATCH_SIZE, train=False
      )
      self._build_model()
      self._build_update_func()
  
imports: import argparse
  import sys
  import tensorflow
  import tf_encrypted
  from tf_encrypted.keras.datasets import *
  from tf_encrypted.protocol import ABY3
  from tf_encrypted.protocol import Pond
  from tf_encrypted.protocol import SecureNN
label_desc: Use image node_id 1 to create a new ModelOwner object  with example: obj = ModelOwner(player_name, model_name, data_name)