code: def test_error_on_incorrect_shape(
      metric_class=ComplexScaleInvariantSignalNoiseRatio,
  ):
      """Test that error is raised on incorrect shapes of input."""
      metric = metric_class()
      with pytest.raises(
          RuntimeError,
          match="Predictions and targets are expected to have the shape (..., frequency, time, 2)*",
      ):
          metric(torch.randn(100), torch.randn(50))
  
file_path: torchmetrics/tests/unittests/audio/test_c_si_snr.py
imports: import pytest
  import torch
  from scipy.io import wavfile
  from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
  from torchmetrics.functional.audio import complex_scale_invariant_signal_noise_ratio
  from unittests import BATCH_SIZE, NUM_BATCHES, _Input
  from unittests.audio import _SAMPLE_AUDIO_SPEECH, _SAMPLE_AUDIO_SPEECH_BAB_DB
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 2 for calling a global function with example usage: test_error_on_incorrect_shape(metric_class) without return types