code: def test_chunk_iterator_stride(self):
      feature_extractor = AutoFeatureExtractor.from_pretrained(
          "facebook/wav2vec2-base-960h"
      )
      inputs = torch.arange(100).long()
      input_values = feature_extractor(
          inputs,
          sampling_rate=feature_extractor.sampling_rate,
          return_tensors="pt",
      )["input_values"]
      ratio = 1
      outs = list(
          chunk_iter(inputs, feature_extractor, 100, 20, 10, ratio)
      )
      self.assertEqual(len(outs), 2)
      self.assertEqual(
          [o["stride"] for o in outs], [(100, 0, 10), (30, 20, 0)]
      )
      self.assertEqual(
          [o["input_values"].shape for o in outs], [(1, 100), (1, 30)]
      )
      self.assertEqual([o["is_last"] for o in outs], [False, True])
  
      outs = list(
          chunk_iter(inputs, feature_extractor, 80, 20, 10, ratio)
      )
      self.assertEqual(len(outs), 2)
      self.assertEqual(
          [o["stride"] for o in outs], [(80, 0, 10), (50, 20, 0)]
      )
      self.assertEqual(
          [o["input_values"].shape for o in outs], [(1, 80), (1, 50)]
      )
      self.assertEqual([o["is_last"] for o in outs], [False, True])
  
      outs = list(
          chunk_iter(inputs, feature_extractor, 90, 20, 0, ratio)
      )
      self.assertEqual(len(outs), 2)
      self.assertEqual(
          [o["stride"] for o in outs], [(90, 0, 0), (30, 20, 0)]
      )
      self.assertEqual(
          [o["input_values"].shape for o in outs], [(1, 90), (1, 30)]
      )
  
      outs = list(
          chunk_iter(inputs, feature_extractor, 36, 6, 6, ratio)
      )
      self.assertEqual(len(outs), 4)
      self.assertEqual(
          [o["stride"] for o in outs],
          [(36, 0, 6), (36, 6, 6), (36, 6, 6), (28, 6, 0)],
      )
      self.assertEqual(
          [o["input_values"].shape for o in outs],
          [(1, 36), (1, 36), (1, 36), (1, 28)],
      )
  
      inputs = torch.LongTensor([i % 2 for i in range(100)])
      input_values = feature_extractor(
          inputs,
          sampling_rate=feature_extractor.sampling_rate,
          return_tensors="pt",
      )["input_values"]
      outs = list(
          chunk_iter(inputs, feature_extractor, 30, 5, 5, ratio)
      )
      self.assertEqual(len(outs), 5)
      self.assertEqual(
          [o["stride"] for o in outs],
          [(30, 0, 5), (30, 5, 5), (30, 5, 5), (30, 5, 5), (20, 5, 0)],
      )
      self.assertEqual(
          [o["input_values"].shape for o in outs],
          [(1, 30), (1, 30), (1, 30), (1, 30), (1, 20)],
      )
      self.assertEqual(
          [o["is_last"] for o in outs],
          [False, False, False, False, True],
      )
      # (0, 25)
      self.assertEqual(
          nested_simplify(input_values[:, :30]),
          nested_simplify(outs[0]["input_values"]),
      )
      # (25, 45)
      self.assertEqual(
          nested_simplify(input_values[:, 20:50]),
          nested_simplify(outs[1]["input_values"]),
      )
      # (45, 65)
      self.assertEqual(
          nested_simplify(input_values[:, 40:70]),
          nested_simplify(outs[2]["input_values"]),
      )
      # (65, 85)
      self.assertEqual(
          nested_simplify(input_values[:, 60:90]),
          nested_simplify(outs[3]["input_values"]),
      )
      # (85, 100)
      self.assertEqual(
          nested_simplify(input_values[:, 80:100]),
          nested_simplify(outs[4]["input_values"]),
      )
  
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 38 for calling the AutomaticSpeechRecognitionPipelineTests obj's underlying member method code with example usage: obj.test_chunk_iterator_stride() without return types