class_docstr: Cover the tests for model which have use_cache feature (i.e. "with_past" for ONNX)
class_docstr_tok: ["Cover","the","tests","for","model","which","have","use_cache","feature","(","i.e",".","``","with_past","''","for","ONNX",")"]
docstr: Ensure the use_past variable is correctly being set
docstr_tok: ["Ensure","the","use_past","variable","is","correctly","being","set"]
code: def test_use_past(self):
      """
      Ensure the use_past variable is correctly being set
      """
      for (
          name,
          config,
      ) in OnnxConfigWithPastTestCaseV2.SUPPORTED_WITH_PAST_CONFIGS:
          with self.subTest(name):
              self.assertFalse(
                  OnnxConfigWithPast.from_model_config(
                      config()
                  ).use_past,
                  "OnnxConfigWithPast.from_model_config() should not use_past",
              )
  
              self.assertTrue(
                  OnnxConfigWithPast.with_past(config()).use_past,
                  "OnnxConfigWithPast.from_model_config() should use_past",
              )
  
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 OnnxConfigWithPastTestCaseV2 obj's underlying member method code with example usage: obj.test_use_past() without return types