code: def test_too_low_precision():
      """Corner case where the precision of the input is important."""
      data = np.load(_SAMPLE_NUMPY_ISSUE_895)
      preds = torch.tensor(data["preds"])
      target = torch.tensor(data["target"])
  
      sdr_tm = signal_distortion_ratio(preds, target).double()
  
      # check equality with bss_eval_sources in every pytorch version
      sdr_bss, _, _, _ = bss_eval_sources(
          target.numpy(), preds.numpy(), False
      )
      assert torch.allclose(
          sdr_tm.mean(),
          torch.tensor(sdr_bss).mean(),
          rtol=0.0001,
          atol=1e-2,
      )
  
imports: from functools import partial
  from typing import Callable
  import numpy
  import pytest
  import torch
  from mir_eval.separation import bss_eval_sources
  from scipy.io import wavfile
  from torch import Tensor
  from torchmetrics.audio import SignalDistortionRatio
  from torchmetrics.functional import signal_distortion_ratio
  from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_11
  from unittests import _Input
  from unittests.audio import _SAMPLE_AUDIO_SPEECH, _SAMPLE_AUDIO_SPEECH_BAB_DB, _SAMPLE_NUMPY_ISSUE_895
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester
label_desc: Use image node_id 5 for calling a global function with example usage: test_too_low_precision() without return types