code: def test_onnx_decoder_model_with_config_with_loss(self):
      self.skipTest(
          "Skipping due to a bug introduced in transformers with https://github.com/huggingface/transformers/pull/24979, argmax on int64 is not supported by ONNX"
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
          # Prepare model and dataset
          model_checkpoint = "hf-internal-testing/tiny-random-gpt2"
          model = AutoModelForSequenceClassification.from_pretrained(
              model_checkpoint
          )
  
          # Wrap OnnxConfig
          onnx_config_constructor = (
              TasksManager.get_exporter_config_constructor(
                  model=model,
                  exporter="onnx",
                  task="text-classification",
              )
          )
          onnx_config = onnx_config_constructor(model.config)
          wrapped_onnx_config = OnnxConfigWithLoss(onnx_config)
  
          # Export model from PyTorch to ONNX
          onnx_model_path = Path(
              os.path.join(tmp_dir, f"{model.config.model_type}.onnx")
          )
          opset = max(onnx_config.DEFAULT_ONNX_OPSET, 12)
          export(
              model=model,
              config=wrapped_onnx_config,
              opset=opset,
              output=onnx_model_path,
          )
  
          # ONNX Runtime Inference
          ort_sess = onnxruntime.InferenceSession(
              onnx_model_path.as_posix(),
              providers=[
                  "CUDAExecutionProvider"
                  if torch.cuda.is_available()
                  and "CUDAExecutionProvider"
                  in onnxruntime.get_available_providers()
                  else "CPUExecutionProvider"
              ],
          )
  
          batch = 3
          seq_length = 8
          inputs = {
              "input_ids": torch.ones(
                  (batch, seq_length), dtype=torch.long
              ),
              "attention_mask": torch.ones(
                  (batch, seq_length), dtype=torch.long
              ),
              "labels": torch.zeros(batch, dtype=torch.long),
          }
          input_names = [
              ort_input.name for ort_input in ort_sess._inputs_meta
          ]
          output_names = [
              output.name for output in ort_sess._outputs_meta
          ]
          input_feed = {
              input_name: inputs[input_name].cpu().numpy()
              for input_name in input_names
          }
          ort_outputs = ort_sess.run(output_names, input_feed)
          pt_outputs = model(**inputs)
  
          # Checkers
          assert (
              len(ort_outputs) > 1
          ), "There is only one element in outputs, the loss might be missing!"
          self.assertAlmostEqual(
              float(ort_outputs[0]),
              float(pt_outputs["loss"]),
              3,
              "The losses of ONNX Runtime and PyTorch inference are not close enough!",
          )
          gc.collect()
  
imports: import gc
  import os
  import tempfile
  import unittest
  from pathlib import Path
  import onnxruntime
  import pytest
  import torch
  from transformers import AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
  from transformers.modeling_utils import PreTrainedModel
  from transformers.utils import is_tf_available
  from optimum.exporters import TasksManager
  from optimum.exporters.onnx import OnnxConfigWithLoss, export
  from optimum.onnxruntime.utils import ONNX_DECODER_NAME
  from optimum.utils import DummyTextInputGenerator
  from optimum.utils.normalized_config import NormalizedConfigManager
label_desc: Use image node_id 2 for calling the TestOnnxConfigWithLoss obj's underlying member method code with example usage: obj.test_onnx_decoder_model_with_config_with_loss() without return types