code: def test_SpecAuc(
      apply_time_warp,
      apply_freq_mask,
      apply_time_mask,
      time_mask_width_range,
      time_mask_width_ratio_range,
  ):
      if (
          (
              not apply_time_warp
              and not apply_time_mask
              and not apply_freq_mask
          )
          or (
              apply_time_mask
              and time_mask_width_range is None
              and time_mask_width_ratio_range is None
          )
          or (
              apply_time_mask
              and time_mask_width_range is not None
              and time_mask_width_ratio_range is not None
          )
      ):
          with pytest.raises(ValueError):
              specaug = SpecAug(
                  apply_time_warp=apply_time_warp,
                  apply_freq_mask=apply_freq_mask,
                  apply_time_mask=apply_time_mask,
                  time_mask_width_range=time_mask_width_range,
                  time_mask_width_ratio_range=time_mask_width_ratio_range,
              )
      else:
          specaug = SpecAug(
              apply_time_warp=apply_time_warp,
              apply_freq_mask=apply_freq_mask,
              apply_time_mask=apply_time_mask,
              time_mask_width_range=time_mask_width_range,
              time_mask_width_ratio_range=time_mask_width_ratio_range,
          )
          x = torch.randn(2, 1000, 80)
          specaug(x)
  
imports: import pytest
  import torch
  from espnet2.asr.specaug.specaug import SpecAug
label_desc: Use image node_id 1 for calling a global function with example usage: test_SpecAuc(apply_time_warp, apply_freq_mask, apply_time_mask, time_mask_width_range, time_mask_width_ratio_range) without return types