code: def _get_models_to_test(export_models_list):
      models_to_test = []
      if is_torch_available() or is_tf_available():
          for name, model, *features in export_models_list:
              if features:
                  feature_config_mapping = {
                      feature: FeaturesManager.get_config(name, feature)
                      for _ in features
                      for feature in _
                  }
              else:
                  feature_config_mapping = FeaturesManager.get_supported_features_for_model_type(
                      name
                  )
  
              for (
                  feature,
                  onnx_config_class_constructor,
              ) in feature_config_mapping.items():
                  models_to_test.append(
                      (
                          f"{name}_{feature}",
                          name,
                          model,
                          feature,
                          onnx_config_class_constructor,
                      )
                  )
          return sorted(models_to_test)
      else:
          # Returning some dummy test that should not be ever called because of the @require_torch / @require_tf
          # decorators.
          # The reason for not returning an empty list is because parameterized.expand complains when it's empty.
          return [
              (
                  "dummy",
                  "dummy",
                  "dummy",
                  "dummy",
                  OnnxConfig.from_model_config,
              )
          ]
  
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 a global function with example usage: _get_models_to_test(export_models_list) and returns: sorted, list