code: def test_onnx_seq2seq_model_with_config_with_loss(self):
      with tempfile.TemporaryDirectory() as tmp_dir:
          # Prepare model and dataset
          model_checkpoint = "hf-internal-testing/tiny-random-t5"
          model = AutoModelForSeq2SeqLM.from_pretrained(
              model_checkpoint
          )
  
          # Wrap OnnxConfig(decoders)
          onnx_config_constructor = (
              TasksManager.get_exporter_config_constructor(
                  model=model,
                  exporter="onnx",
                  task="text2text-generation",
              )
          )
          onnx_config = onnx_config_constructor(model.config)
  
          onnx_config_decoder = onnx_config.with_behavior(
              "decoder", use_past=False
          )
  
          wrapped_onnx_config_decoder = OnnxConfigWithLoss(
              onnx_config_decoder
          )
  
          # Export decoder models from PyTorch to ONNX
          opset = max(onnx_config.DEFAULT_ONNX_OPSET, 12)
  
          onnx_model_path = Path(tmp_dir).joinpath(ONNX_DECODER_NAME)
  
          export(
              model=model,
              config=wrapped_onnx_config_decoder,
              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
          encoder_seq_length = 8
          encoder_hidden_states_shape = (
              batch,
              encoder_seq_length,
              model.config.hidden_size,
          )
          inputs = {
              "input_ids": torch.ones(
                  (batch, encoder_seq_length), dtype=torch.long
              ),
              "encoder_hidden_states": torch.zeros(
                  encoder_hidden_states_shape
              ),
              "encoder_attention_mask": torch.ones(
                  (batch, encoder_seq_length), dtype=torch.long
              ),
              "labels": torch.zeros(
                  (batch, encoder_seq_length), 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_sess.run(output_names, input_feed)
  
          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 3 for calling the TestOnnxConfigWithLoss obj's underlying member method code with example usage: obj.test_onnx_seq2seq_model_with_config_with_loss() without return types