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: Compute gradient given current model parameters and local data.
docstr_tok: ["Compute","gradient","given","current","model","parameters","and","local","data","."]
code: def compute_gradient(self):
      """Compute gradient given current model parameters and local data."""
      return self.gradient_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 5 for calling the DataOwner obj's underlying member method code with example usage: obj.compute_gradient() and returns: self