code: def _smoke_test(self, model, device, dtype):
      model = model.to(device=device, dtype=dtype)
      model = model.eval()
  
      batch_size, num_frames = 3, 1024
  
      waveforms = torch.randn(
          batch_size, num_frames, device=device, dtype=dtype
      )
      lengths = torch.randint(
          low=0,
          high=num_frames,
          size=[
              batch_size,
          ],
          device=device,
      )
  
      model(waveforms, lengths)
  
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 1 for calling the TestWav2Vec2Model obj's underlying member method code with example usage: obj._smoke_test(model, device, dtype) without return types