code: def test_IntentSlotClassificationModel_export_to_onnx(
      self, dummy_data
  ):
      with tempfile.TemporaryDirectory() as tmpdir:
          self.setup_method()
          config = self.dict_config
          config.model.data_dir = dummy_data
          trainer = pl.Trainer(**config.trainer)
          model = IntentSlotClassificationModel(
              config.model, trainer=trainer
          )
          filename = os.path.join(tmpdir, "isc.onnx")
          model.export(output=filename, check_trace=True)
          onnx_model = onnx.load(filename)
          onnx.checker.check_model(
              onnx_model, full_check=True
          )  # throws when failed
          assert onnx_model.graph.input[0].name == "input_ids"
          assert onnx_model.graph.input[1].name == "attention_mask"
          assert onnx_model.graph.input[2].name == "token_type_ids"
          assert onnx_model.graph.output[0].name == "intent_logits"
          assert onnx_model.graph.output[1].name == "slot_logits"
  
imports: import os
  import tempfile
  import onnx
  import pytest
  import pytorch_lightning
  import torch
  import wget
  from omegaconf import DictConfig, OmegaConf
  from nemo.collections import nlp
  from nemo.collections.nlp.models import IntentSlotClassificationModel
  from nemo.collections.nlp.modules.common import SequenceClassifier, SequenceRegression, SequenceTokenClassifier, TokenClassifier
label_desc: Use image node_id 7 for calling the TestExportableClassifiers obj's underlying member method code with example usage: obj.test_IntentSlotClassificationModel_export_to_onnx(dummy_data) without return types