def _load_eval_run(output_path: Text) -> Tuple[(config.EvalConfig, Text, Text, Dict[(Text, Text)])]:
    'Returns eval config, data location, file format, and model locations.'
    path = os.path.join(output_path, _EVAL_CONFIG_FILE)
    if tf.io.gfile.exists(path):
        with tf.io.gfile.GFile(path, 'r') as f:
            pb = json_format.Parse(f.read(), config_pb2.EvalRun())
            _check_version(pb.version, output_path)
            return (pb.eval_config, pb.data_location, pb.file_format, pb.model_locations)
    else:
        path = os.path.splitext(path)[0]
        serialized_record = six.next(tf.compat.v1.python_io.tf_record_iterator(path))
        final_dict = pickle.loads(serialized_record)
        _check_version(final_dict, output_path)
        old_config = final_dict['eval_config']
        slicing_specs = None
        if old_config.slice_spec:
            slicing_specs = [s.to_proto() for s in old_config.slice_spec]
        options = config.Options()
        options.compute_confidence_intervals.value = old_config.compute_confidence_intervals
        options.k_anonymization_count.value = old_config.k_anonymization_count
        return (config.EvalConfig(slicing_specs=slicing_specs, options=options), old_config.data_location, '', {'': old_config.model_location})
Returns eval config, data location, file format, and model locations.
def _load_eval_run(output_path: Text) -> Tuple[(config.EvalConfig, Text, Text, Dict[(Text, Text)])]:
    
    path = os.path.join(output_path, _EVAL_CONFIG_FILE)
    if tf.io.gfile.exists(path):
        with tf.io.gfile.GFile(path, 'r') as f:
            pb = json_format.Parse(f.read(), config_pb2.EvalRun())
            _check_version(pb.version, output_path)
            return (pb.eval_config, pb.data_location, pb.file_format, pb.model_locations)
    else:
        path = os.path.splitext(path)[0]
        serialized_record = six.next(tf.compat.v1.python_io.tf_record_iterator(path))
        final_dict = pickle.loads(serialized_record)
        _check_version(final_dict, output_path)
        old_config = final_dict['eval_config']
        slicing_specs = None
        if old_config.slice_spec:
            slicing_specs = [s.to_proto() for s in old_config.slice_spec]
        options = config.Options()
        options.compute_confidence_intervals.value = old_config.compute_confidence_intervals
        options.k_anonymization_count.value = old_config.k_anonymization_count
        return (config.EvalConfig(slicing_specs=slicing_specs, options=options), old_config.data_location, , {: old_config.model_location})