class_docstr: Calculate `Scale-invariant signal-to-noise ratio`_ (SI-SNR) metric for evaluating quality of audio.
  
  As input to `forward` and `update` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
  - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``si_snr`` (:class:`~torch.Tensor`): float scalar tensor with average SI-SNR value over samples
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      TypeError:
          if target and preds have a different shape
  
  Example:
      >>> import torch
      >>> from torch import tensor
      >>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
      >>> target = tensor([3.0, -0.5, 2.0, 7.0])
      >>> preds = tensor([2.5, 0.0, 2.0, 8.0])
      >>> si_snr = ScaleInvariantSignalNoiseRatio()
      >>> si_snr(preds, target)
      tensor(15.0918)
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docstr: Update state with predictions and targets.
docstr_tok: ["Update","state","with","predictions","and","targets","."]
code: def update(self, preds: Tensor, target: Tensor) -> None:
      """Update state with predictions and targets."""
      si_snr_batch = scale_invariant_signal_noise_ratio(
          preds=preds, target=target
      )
  
      self.sum_si_snr += si_snr_batch.sum()
      self.total += si_snr_batch.numel()
  
imports: from typing import Any, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.audio.snr import complex_scale_invariant_signal_noise_ratio, scale_invariant_signal_noise_ratio, signal_noise_ratio
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 2 for calling the ScaleInvariantSignalNoiseRatio obj's underlying member method code with example usage: obj.update(preds, target) without return types