code: def num_layers(self) -> Tuple[int]:
      try:
          num_layers = super().num_layers
          num_layers = (num_layers, num_layers)
      except AttributeError:
          if hasattr(self._config, "encoder_layers") and hasattr(
              self._config, "decoder_layers"
          ):
              num_layers = (
                  self._config.encoder_layers,
                  self._config.decoder_layers,
              )
          else:
              raise AttributeError(
                  "could not find the number of encoder and decoder layers attributes in the model configuration,"
                  " override the num_layers property of the model OnnxConfig to solve this"
              )
  
      return num_layers
  
imports: import copy
  import dataclasses
  import warnings
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union
  import numpy
  from packaging import version
  from ..utils import TensorType, is_torch_available, is_vision_available, logging
  from .utils import ParameterFormat, compute_effective_axis_dimension, compute_serialized_parameters_size
label_desc: Use image node_id 2 for calling the OnnxSeq2SeqConfigWithPast obj's underlying member method code with example usage: obj.num_layers() and returns: num_layers