code: def pipeline(
      task: str = None,
      model: Optional[Any] = None,
      tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None,
      feature_extractor: Optional[
          Union[str, PreTrainedFeatureExtractor]
      ] = None,
      use_fast: bool = True,
      token: Optional[Union[str, bool]] = None,
      accelerator: Optional[str] = "ort",
      revision: Optional[str] = None,
      trust_remote_code: Optional[bool] = None,
      *model_kwargs,
      **kwargs,
  ) -> Pipeline:
      targeted_task = (
          "translation" if task.startswith("translation") else task
      )
  
      if accelerator == "ort":
          if targeted_task not in list(ORT_SUPPORTED_TASKS.keys()):
              raise ValueError(
                  f"Task {targeted_task} is not supported for the ONNX Runtime pipeline. Supported tasks are { list(ORT_SUPPORTED_TASKS.keys())}"
              )
  
      if accelerator not in MAPPING_LOADING_FUNC:
          raise ValueError(
              f'Accelerator {accelerator} is not supported. Supported accelerators are "ort" and "bettertransformer".'
          )
  
      # copied from transformers.pipelines.__init__.py
      hub_kwargs = {
          "revision": revision,
          "token": token,
          "trust_remote_code": trust_remote_code,
          "_commit_hash": None,
      }
  
      config = kwargs.get("config", None)
      if config is None and isinstance(model, str):
          config = AutoConfig.from_pretrained(
              model, _from_pipeline=task, **hub_kwargs, **kwargs
          )
          hub_kwargs["_commit_hash"] = config._commit_hash
  
      supported_tasks = (
          ORT_SUPPORTED_TASKS
          if accelerator == "ort"
          else TRANSFORMERS_SUPPORTED_TASKS
      )
  
      no_feature_extractor_tasks = set()
      no_tokenizer_tasks = set()
      for _task, values in supported_tasks.items():
          if values["type"] == "text":
              no_feature_extractor_tasks.add(_task)
          elif values["type"] in {"image", "video"}:
              no_tokenizer_tasks.add(_task)
          elif values["type"] in {"audio"}:
              no_tokenizer_tasks.add(_task)
          elif values["type"] not in ["multimodal", "audio", "video"]:
              raise ValueError(
                  f"SUPPORTED_TASK {_task} contains invalid type {values['type']}"
              )
  
      # copied from transformers.pipelines.__init__.py l.609
      if targeted_task in no_tokenizer_tasks:
          # These will never require a tokenizer.
          # the model on the other hand might have a tokenizer, but
          # the files could be missing from the hub, instead of failing
          # on such repos, we just force to not load it.
          load_tokenizer = False
      else:
          load_tokenizer = True
  
      if targeted_task in no_feature_extractor_tasks:
          load_feature_extractor = False
      else:
          load_feature_extractor = True
  
      (
          model,
          model_id,
          tokenizer,
          feature_extractor,
      ) = MAPPING_LOADING_FUNC[accelerator](
          model,
          targeted_task,
          load_tokenizer,
          tokenizer,
          feature_extractor,
          load_feature_extractor,
          SUPPORTED_TASKS=supported_tasks,
          config=config,
          hub_kwargs=hub_kwargs,
          token=token,
          *model_kwargs,
          **kwargs,
      )
  
      if tokenizer is None and load_tokenizer:
          tokenizer = get_preprocessor(model_id)
      if feature_extractor is None and load_feature_extractor:
          feature_extractor = get_preprocessor(model_id)
  
      return transformers_pipeline(
          task,
          model=model,
          tokenizer=tokenizer,
          feature_extractor=feature_extractor,
          use_fast=use_fast,
          **kwargs,
      )
  
file_path: optimum/optimum/pipelines/pipelines_base.py
imports: from typing import Any, Dict, Optional, Union
  from transformers import AudioClassificationPipeline, AutoConfig, AutomaticSpeechRecognitionPipeline, FeatureExtractionPipeline, FillMaskPipeline, ImageClassificationPipeline, ImageSegmentationPipeline, ImageToTextPipeline, Pipeline, PreTrainedTokenizer, PreTrainedTokenizerFast, QuestionAnsweringPipeline, SequenceFeatureExtractor, SummarizationPipeline, Text2TextGenerationPipeline, TextClassificationPipeline, TextGenerationPipeline, TokenClassificationPipeline, TranslationPipeline, ZeroShotClassificationPipeline
  from transformers import pipeline
  from transformers.feature_extraction_utils import PreTrainedFeatureExtractor
  from transformers.onnx.utils import get_preprocessor
  from transformers.pipelines import SUPPORTED_TASKS
  from transformers.pipelines import infer_framework_load_model
  from ..bettertransformer import BetterTransformer
  from ..utils import is_onnxruntime_available
  from ..utils.file_utils import find_files_matching_pattern
label_desc: Use image node_id 3 for calling a global function with example usage: pipeline(task, model, tokenizer, feature_extractor, use_fast, token, accelerator, revision, trust_remote_code) and returns: transformers_pipeline