class_docstr: Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
docstr: Instantiate a OnnxConfig for a specific model
  
  Args:
      config: The model's configuration to use when exporting to ONNX
  
  Returns:
      OnnxConfig for this model
docstr_tok: ["Instantiate","a","OnnxConfig","for","a","specific","model","Args",":","config",":","The","model","'s","configuration","to","use","when","exporting","to","ONNX","Returns",":","OnnxConfig","for","this","model"]
code: def from_model_config(
      cls, config: "PretrainedConfig", task: str = "default"
  ) -> "OnnxConfig":
      """
      Instantiate a OnnxConfig for a specific model
  
      Args:
          config: The model's configuration to use when exporting to ONNX
  
      Returns:
          OnnxConfig for this model
      """
      return cls(config, task=task)
  
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 OnnxConfig obj's underlying member method code with example usage: obj.from_model_config(cls, config, task) and returns: cls