code: def _test_batch_consistency(self, model):
      model.eval()
      batch_size, max_frames = 5, 5 * 1024
      waveforms = torch.randn(batch_size, max_frames)
      input_lengths = torch.tensor([i * 3200 for i in range(1, 6)])
  
      # Batch process with lengths
      batch_logits, output_lengths = model(waveforms, input_lengths)
      for i in range(batch_size):
          # Par-sample process without feeding length
          single_logit, _ = model(
              waveforms[i : i + 1, : input_lengths[i]], None
          )
          batch_logit = batch_logits[i : i + 1, : output_lengths[i]]
  
          # Convert to probability so that it's easier to interpretate the diff
          single_prob = F.softmax(single_logit, dim=2)
          batch_prob = F.softmax(batch_logit, dim=2)
          # We allow max atol=0.005 -> 0.5%
          self.assertEqual(single_prob, batch_prob, atol=0.005, rtol=0)
  
imports: import math
  import os
  from typing import Tuple
  import torch
  import torch.nn.functional
  from parameterized import parameterized
  from torchaudio.models.wav2vec2 import hubert_base, hubert_large, hubert_pretrain_base, hubert_pretrain_large, hubert_pretrain_xlarge, hubert_xlarge, wav2vec2_base, wav2vec2_large, wav2vec2_large_lv60k, wavlm_base, wavlm_large
  from torchaudio_unittest.common_utils import skipIfNoCuda, skipIfNoQengine, torch_script, TorchaudioTestCase
label_desc: Use image node_id 6 for calling the TestWav2Vec2Model obj's underlying member method code with example usage: obj._test_batch_consistency(model) without return types