code: def _test_batch_consistency(self, model):
      model.eval()
      batch_size, max_frames = 5, 5 * 1024
      waveforms = torch.randn(batch_size, max_frames)
  
      # Batch process
      batch_logits, _ = model(waveforms)
      # Par-sample process
      for i in range(batch_size):
          single_logit, _ = model(waveforms[i : i + 1])
          batch_logit = batch_logits[i : i + 1]
  
          # 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 4 for calling the TestWavLMModel obj's underlying member method code with example usage: obj._test_batch_consistency(model) without return types