code: def signal_noise_ratio(
      preds: Tensor, target: Tensor, zero_mean: bool = False
  ) -> Tensor:
      r"""Calculate `Signal-to-noise ratio`_ (SNR_) meric for evaluating quality of audio.
  
      .. math::
          \text{SNR} = \frac{P_{signal}}{P_{noise}}
  
      where  :math:`P` denotes the power of each signal. The SNR metric compares the level of the desired signal to
      the level of background noise. Therefore, a high value of SNR means that the audio is clear.
  
      Args:
          preds: float tensor with shape ``(...,time)``
          target: float tensor with shape ``(...,time)``
          zero_mean: if to zero mean target and preds or not
  
      Returns:
          Float tensor with shape ``(...,)`` of SNR values per sample
  
      Raises:
          RuntimeError:
              If ``preds`` and ``target`` does not have the same shape
  
      Example:
          >>> from torchmetrics.functional.audio import signal_noise_ratio
          >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
          >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
          >>> signal_noise_ratio(preds, target)
          tensor(16.1805)
  
      """
      _check_same_shape(preds, target)
      eps = torch.finfo(preds.dtype).eps
  
      if zero_mean:
          target = target - torch.mean(target, dim=-1, keepdim=True)
          preds = preds - torch.mean(preds, dim=-1, keepdim=True)
  
      noise = target - preds
  
      snr_value = (torch.sum(target**2, dim=-1) + eps) / (
          torch.sum(noise**2, dim=-1) + eps
      )
      return 10 * torch.log10(snr_value)
  
imports: import torch
  from torch import Tensor
  from torchmetrics.functional.audio.sdr import scale_invariant_signal_distortion_ratio
  from torchmetrics.utilities.checks import _check_same_shape
label_desc: Use image node_id 1 for calling a global function with example usage: signal_noise_ratio(preds, target, zero_mean) and returns: unknown