code: def fixture_sentiment() -> (
      tuple[transformers.Pipeline, TaskDefinition]
  ):
      alias, original_task = t.cast(
          "tuple[t.Literal['text-classification'], TaskDefinition, t.Any]",
          check_task("sentiment-analysis"),
      )[:2]
      return (
          pipeline(
              alias, model="hf-internal-testing/tiny-random-distilbert"
          ),
          original_task,
      )
  
imports: from __future__ import annotations
  import logging
  import os
  import typing
  import pytest
  import transformers
  from transformers.models.auto.modeling_auto import AutoModelForAudioClassification
  from transformers.models.auto.modeling_auto import AutoModelForCausalLM
  from transformers.models.auto.modeling_auto import AutoModelForSequenceClassification
  from transformers.pipelines import check_task
  from transformers.pipelines import pipeline
  from transformers.pipelines.audio_classification import AudioClassificationPipeline
  from transformers.trainer_utils import set_seed
  import bentoml
  from bentoml.exceptions import BentoMLException
  from bentoml.transformers import ModelOptions
label_desc: Use image node_id 3 for calling a global function with example usage: fixture_sentiment() and returns: pipeline,  original_task