class_docstr: Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
docstr: The minimum PyTorch version required to export the model.
  
  Returns:
      `bool`: Whether the installed version of PyTorch is compatible with the model.
docstr_tok: ["The","minimum","PyTorch","version","required","to","export","the","model",".","Returns",":","`","bool","`",":","Whether","the","installed","version","of","PyTorch","is","compatible","with","the","model","."]
code: def is_torch_support_available(self) -> bool:
      """
      The minimum PyTorch version required to export the model.
  
      Returns:
          `bool`: Whether the installed version of PyTorch is compatible with the model.
      """
      if is_torch_available():
          from transformers.utils import torch_version
  
          return torch_version >= self.torch_onnx_minimum_version
      else:
          return False
  
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 11 for calling the OnnxConfig obj's underlying member method code with example usage: obj.is_torch_support_available() and returns: unknown, False