class_docstr: Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
docstr: Mapping containing the axis definition of the input tensors to provide to the model
  
  Returns:
      For each input: its name associated to the axes symbolic name and the axis position within the tensor
docstr_tok: ["Mapping","containing","the","axis","definition","of","the","input","tensors","to","provide","to","the","model","Returns",":","For","each","input",":","its","name","associated","to","the","axes","symbolic","name","and","the","axis","position","within","the","tensor"]
code: def inputs(self) -> Mapping[str, Mapping[int, str]]:
      """
      Mapping containing the axis definition of the input tensors to provide to the model
  
      Returns:
          For each input: its name associated to the axes symbolic name and the axis position within the tensor
      """
      raise NotImplementedError()
  
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 OnnxConfig obj's underlying member method code with example usage: obj.inputs() without return types