class_docstr: Cover all the utilities involved to export ONNX models
class_docstr_tok: ["Cover","all","the","utilities","involved","to","export","ONNX","models"]
docstr: Ensure we raise an Exception if the pytorch version is unsupported (< 1.8.0)
docstr_tok: ["Ensure","we","raise","an","Exception","if","the","pytorch","version","is","unsupported","(","<","1.8.0",")"]
code: def test_ensure_pytorch_version_ge_1_8_0(
      self, mock_is_torch_onnx_dict_inputs_support_available
  ):
      """
      Ensure we raise an Exception if the pytorch version is unsupported (< 1.8.0)
      """
      self.assertRaises(
          AssertionError, export, None, None, None, None, None
      )
      mock_is_torch_onnx_dict_inputs_support_available.assert_called()
  
imports: import os
  from pathlib import Path
  from tempfile import NamedTemporaryFile
  from unittest import TestCase
  from unittest.mock import patch
  import pytest
  from parameterized import parameterized
  from transformers import AutoConfig, PreTrainedTokenizerBase, is_tf_available, is_torch_available
  from transformers.onnx import EXTERNAL_DATA_FORMAT_SIZE_LIMIT, OnnxConfig, OnnxConfigWithPast, ParameterFormat, export, validate_model_outputs
  from transformers.onnx.utils import compute_effective_axis_dimension, compute_serialized_parameters_size, get_preprocessor
  from transformers.testing_utils import require_onnx, require_rjieba, require_tf, require_torch, require_vision, slow
label_desc: Use image node_id 1 for calling the OnnxUtilsTestCaseV2 obj's underlying member method code with example usage: obj.test_ensure_pytorch_version_ge_1_8_0(mock_is_torch_onnx_dict_inputs_support_available) without return types