code: def test_triton_exportability(config_path, tmpdir):
      """Tests whether Triton export succeeds for a config."""
      config = load_yaml(config_path)
      dataset = build_synthetic_dataset_df(100, config)
      ludwig_model = LudwigModel(config)
      ludwig_model.train(
          dataset=dataset,
          skip_save_training_description=True,
          skip_save_training_statistics=True,
          skip_save_model=True,
          skip_save_progress=True,
          skip_save_log=True,
          skip_save_processed_input=True,
      )
  
      triton_path = os.path.join(tmpdir, "triton")
      model_name = "test_triton"
      model_version = "1"
      export_triton(
          model=ludwig_model,
          data_example=dataset.head(10),
          model_name=model_name,
          output_path=triton_path,
          model_version=model_version,
      )
  
file_path: ludwig/tests/integration_tests/test_triton.py
imports: import os
  from typing import List
  import pandas
  import pytest
  import torch
  from ludwig.api import LudwigModel
  from ludwig.constants import BATCH_SIZE, TRAINER
  from ludwig.data.dataset_synthesizer import build_synthetic_dataset_df
  from ludwig.utils.data_utils import load_yaml
  from ludwig.utils.inference_utils import to_inference_module_input_from_dataframe
  from ludwig.utils.triton_utils import export_triton, get_inference_modules, POSTPROCESSOR, PREDICTOR, PREPROCESSOR
  from tests.integration_tests.utils import binary_feature, category_feature, generate_data, LocalTestBackend, number_feature, sequence_feature, set_feature, text_feature, vector_feature
label_desc: Use image node_id 3 for calling a global function with example usage: test_triton_exportability(config_path, tmpdir) without return types