code: def export_triton(
      model: LudwigModel,
      data_example: Optional[pd.DataFrame] = None,
      output_path: Optional[str] = "model_repository",
      model_name: Optional[str] = "ludwig_model",
      model_version: Optional[Union[int, str]] = 1,
      preprocessor_num_instances: Optional[int] = 1,
      predictor_device_type: Optional[str] = "cpu",
      predictor_num_instances: Optional[int] = 1,
      postprocessor_num_instances: Optional[int] = 1,
      predictor_max_batch_size: Optional[int] = 64,
      max_queue_delay_microseconds: Optional[int] = 100,
  ) -> List[TritonArtifact]:
      """Exports a torchscript model to a output path that serves as a repository for Triton Inference Server.
  
      # Inputs
      :param model: (LudwigModel) A ludwig model.
      :param data_example: (pd.DataFrame) an example from the dataset.
          Used to get dimensions throughout the pipeline.
      :param output_path: (str) The output path for the model repository.
      :param model_name: (str) The optional model name.
      :param model_version: (Union[int,str]) The optional model verison.
      :param preprocessor_num_instances: (int) number of instances for the preprocessor (on CPU).
      :param predictor_device_type: (str) device type for the predictor to be deployed on. One of "cpu" or "cuda"
      :param predictor_num_instances: (int) number of instances for the predictor.
      :param postprocessor_num_instances: (int) number of instances for the postprocessor (on CPU).
      :param predictor_max_batch_size: (int) max_batch_size parameter for the predictor Triton config.
      :param max_queue_delay_microseconds: (int) max_queue_delay_microseconds for all Triton configs.
  
      # Return
      :return: (List[TritonArtifact]) list of TritonArtifacts that contains information about exported artifacts.
      """
  
      device_types, instance_counts = get_device_types_and_counts(
          preprocessor_num_instances,
          predictor_device_type,
          predictor_num_instances,
          postprocessor_num_instances,
      )
      split_modules = get_inference_modules(model, device_types[1])
      example_input = get_example_input(
          model, device_types, data_example
      )
  
      triton_masters = []
      triton_artifacts = []
      for i, module in enumerate(split_modules):
          example_input = place_on_device(
              example_input, device_types[i]
          )
          triton_master = TritonMaster(
              module=module,
              input_data_example=example_input,
              inference_stage=INFERENCE_STAGES[i],
              max_batch_size=predictor_max_batch_size,
              max_queue_delay_microseconds=max_queue_delay_microseconds,
              model_name=model_name,
              output_path=output_path,
              model_version=model_version,
              ludwig_config=model.config,
              device=device_types[i],
              model_instance_count=instance_counts[i],
          )
          example_input = triton_master.output_data_example
          config_artifact = triton_master.save_config()
          model_artifact = triton_master.save_model()
          triton_masters.append(triton_master)
          triton_artifacts.extend([config_artifact, model_artifact])
  
      # saving ensemble config
      (
          triton_master_preprocessor,
          triton_master_predictor,
          triton_master_postprocessor,
      ) = triton_masters
      ensemble_config = TritonEnsembleConfig(
          triton_master_preprocessor,
          triton_master_predictor,
          triton_master_postprocessor,
          model_name,
          output_path,
          model_version,
      )
      config_artifact = ensemble_config.save_ensemble_config()
      model_artifact = ensemble_config.save_ensemble_dummy_model()
      triton_artifacts.extend([config_artifact, model_artifact])
      clean_up_synthetic_data()
  
      return triton_artifacts
  
imports: import importlib.util
  import os
  import re
  import shutil
  import tempfile
  from dataclasses import dataclass
  from typing import Dict, List, Optional, Tuple, Union
  import pandas
  import torch
  from ludwig.api import LudwigModel
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import AUDIO, BAG, BINARY, CATEGORY, DATE, IMAGE, INPUT_FEATURES, POSTPROCESSOR, PREDICTOR, PREPROCESSOR, SEQUENCE, SET, TEXT, TIMESERIES, TYPE, VECTOR
  from ludwig.data.dataset_synthesizer import build_synthetic_dataset
  from ludwig.models.inference import _InferencePostprocessor, _InferencePredictor, _InferencePreprocessor, InferenceModule
  from ludwig.types import ModelConfigDict
  from ludwig.utils.inference_utils import to_inference_module_input_from_dataframe
  from ludwig.utils.misc_utils import remove_empty_lines
  from ludwig.utils.torch_utils import model_size, place_on_device
  from ludwig.utils.types import TorchAudioTuple, TorchscriptPreprocessingInput
label_desc: Use image node_id 8 for calling a global function with example usage: export_triton(model, data_example, output_path, model_name, model_version, preprocessor_num_instances, predictor_device_type, predictor_num_instances, postprocessor_num_instances, predictor_max_batch_size, max_queue_delay_microseconds) and returns: triton_artifacts