code: def num_attention_heads(self) -> Tuple[int]:
      try:
          num_attention_heads = super().num_attention_heads
          num_attention_heads = (
              num_attention_heads,
              num_attention_heads,
          )
      except AttributeError:
          if hasattr(
              self._config, "encoder_attention_heads"
          ) and hasattr(self._config, "decoder_attention_heads"):
              num_attention_heads = (
                  self._config.encoder_attention_heads,
                  self._config.decoder_attention_heads,
              )
          else:
              raise AttributeError(
                  "could not find the number of attention heads for the encoder and the decoder attributes in the"
                  " model configuration, override the num_attention_heads property of the model OnnxConfig to solve"
                  " this"
              )
      return num_attention_heads
  
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 3 for calling the OnnxSeq2SeqConfigWithPast obj's underlying member method code with example usage: obj.num_attention_heads() and returns: num_attention_heads