code: def test_xls_r_to_en(self):
      speech_recognizer = pipeline(
          task="automatic-speech-recognition",
          model="facebook/wav2vec2-xls-r-1b-21-to-en",
          feature_extractor="facebook/wav2vec2-xls-r-1b-21-to-en",
          framework="pt",
      )
  
      ds = load_dataset(
          "hf-internal-testing/librispeech_asr_dummy",
          "clean",
          split="validation",
      ).sort("id")
      filename = ds[40]["file"]
      output = speech_recognizer(filename)
      self.assertEqual(
          output, {"text": "A man said to the universe: “Sir, I exist."}
      )
  
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 24 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.test_xls_r_to_en() without return types