human
I wrote the below code

    if (not re.match('^[a-zA-Z][-A-Za-z0-9]*$', task_prefix)):
        raise AirflowException(('Malformed    task_id    for    DataFlowPythonOperator    (only    alphanumeric    and    hyphens    are    allowed    but    got:    ' + task_prefix))
    (metric_fn, metric_keys) = metric_fn_and_keys
    if (not callable(metric_fn)):
        raise AirflowException('`metric_fn`    param    must    be    callable.')
    if (not callable(validate_fn)):
        raise AirflowException('`validate_fn`    param    must    be    callable.')
    if ((dag is not None) and (dag.default_args is not None)):
        default_args = dag.default_args
        project_id = (project_id or default_args.get('project_id'))
        region = (region or default_args.get('region'))
        model_name = (model_name or default_args.get('model_name'))
        version_name = (version_name or default_args.get('version_name'))
        dataflow_options = (dataflow_options or default_args.get('dataflow_default_options'))
    evaluate_prediction = CloudMLBatchPredictionOperator(task_id=(task_prefix + '-prediction'), project_id=project_id, job_id=batch_prediction_job_id, region=region, data_format=data_format, input_paths=input_paths, output_path=prediction_path, uri=model_uri, model_name=model_name, version_name=version_name, dag=dag)
    metric_fn_encoded = base64.b64encode(dill.dumps(metric_fn, recurse=True))
    evaluate_summary = DataFlowPythonOperator(task_id=(task_prefix + '-summary'), py_options=['-m'], py_file='airflow.contrib.operators.cloudml_prediction_summary', dataflow_default_options=dataflow_options, options={'prediction_path': prediction_path, 'metric_fn_encoded': metric_fn_encoded, 'metric_keys': ','.join(metric_keys)}, dag=dag)
    evaluate_summary.set_upstream(evaluate_prediction)
    def apply_validate_fn(*args, **kwargs):
        prediction_path = kwargs['templates_dict']['prediction_path']
        (scheme, bucket, obj, _, _) = urlsplit(prediction_path)
        if ((scheme != 'gs') or (not bucket) or (not obj)):
            raise...

What's a good function header?
assistant
#Here is Python program to fix + '-prediction'), project_id=project_id, job_id=batch_prediction_job_id, region=region, data_format=data_format, input_paths=input_paths, output_path=prediction_path, uri=model_uri, model_name=model_name, version_name=version_name, dag=dag)
.

def create_evaluate_ops(task_prefix, data_format, input_paths, prediction_path, metric_fn_and_keys, validate_fn, batch_prediction_job_id=None, project_id=None, region=None, dataflow_options=None, model_uri=None, model_name=None, version_name=None, dag=None):