code: def get_test_pipeline(self, model, tokenizer, processor):
      if tokenizer is None:
          # Side effect of no Fast Tokenizer class for these model, so skipping
          # But the slow tokenizer test should still run as they're quite small
          self.skipTest("No tokenizer available")
          return
          # return None, None
  
      speech_recognizer = AutomaticSpeechRecognitionPipeline(
          model=model, tokenizer=tokenizer, feature_extractor=processor
      )
  
      # test with a raw waveform
      audio = np.zeros((34000,))
      audio2 = np.zeros((14000,))
      return speech_recognizer, [audio, audio2]
  
imports: import unittest
  import numpy
  import pytest
  from datasets import Audio, load_dataset
  from huggingface_hub import hf_hub_download, snapshot_download
  from transformers import MODEL_FOR_CTC_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, AutoFeatureExtractor, AutoProcessor, AutoTokenizer, Speech2TextForConditionalGeneration, Wav2Vec2ForCTC, WhisperForConditionalGeneration
  from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
  from transformers.pipelines.audio_utils import chunk_bytes_iter
  from transformers.pipelines.automatic_speech_recognition import _find_timestamp_sequence, chunk_iter
  from transformers.testing_utils import is_pipeline_test, is_torch_available, nested_simplify, require_pyctcdecode, require_tf, require_torch, require_torch_accelerator, require_torchaudio, slow, torch_device
  from .test_pipelines_common import ANY
label_desc: Use image node_id 1 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.get_test_pipeline(model, tokenizer, processor) and returns: speech_recognizer,  list