class_docstr: `Source-aggregated signal-to-distortion ratio`_ (SA-SDR).
  
  The SA-SDR is proposed to provide a stable gradient for meeting style source separation, where
  one-speaker and multiple-speaker scenes coexist.
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(..., spk, time)``
  - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(..., spk, time)``
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``sa_sdr`` (:class:`~torch.Tensor`): float scalar tensor with average SA-SDR value over samples
  
  Args:
      preds: float tensor with shape ``(..., spk, time)``
      target: float tensor with shape ``(..., spk, time)``
      scale_invariant: if True, scale the targets of different speakers with the same alpha
      zero_mean: If to zero mean target and preds or not
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> import torch
      >>> from torchmetrics.audio import SourceAggregatedSignalDistortionRatio
      >>> g = torch.manual_seed(1)
      >>> preds = torch.randn(2, 8000) # [..., spk, time]
      >>> target = torch.randn(2, 8000)
      >>> sasdr = SourceAggregatedSignalDistortionRatio()
      >>> sasdr(preds, target)
      tensor(-41.6579)
      >>> # use with pit
      >>> from torchmetrics.audio import PermutationInvariantTraining
      >>> from torchmetrics.functional.audio import source_aggregated_signal_distortion_ratio
      >>> preds = torch.randn(4, 2, 8000)  # [batch, spk, time]
      >>> target = torch.randn(4, 2, 8000)
      >>> pit = PermutationInvariantTraining(source_aggregated_signal_distortion_ratio,
      ...     mode="permutation-wise", eval_func="max")
      >>> pit(preds, target)
      tensor(-41.2790)
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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."""
      mbatch = source_aggregated_signal_distortion_ratio(
          preds, target, self.scale_invariant, self.zero_mean
      )
  
      self.msum += mbatch.sum()
      self.mnum += mbatch.numel()
  
imports: from typing import Any, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.audio.sdr import scale_invariant_signal_distortion_ratio, signal_distortion_ratio, source_aggregated_signal_distortion_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 SourceAggregatedSignalDistortionRatio obj's underlying member method code with example usage: obj.update(preds, target) without return types