class_docstr: Contains methods meant to be executed by a data owner.
  
  Args:
    player_name: `str`, name of the `tfe.player.Player`
                 representing the data owner
    model_name: `str`, name of the model to be trained
    data_name: `str`, name of dataset that model trained on
    data_slice: specify which part of this dataset to use
docstr: update model with new weights from model owner
docstr_tok: ["update","model","with","new","weights","from","model","owner"]
code: def update_model(self, *update_weights):
      """update model with new weights from model owner"""
      with tf.device(self.player.device_name):
          weights = self.model.weights
          with tf.name_scope("update"):
              for weight, update_weight in zip(weights, update_weights):
                  weight.assign(update_weight)
  
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 6 for calling the DataOwner obj's underlying member method code with example usage: obj.update_model() without return types