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: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting a single value
      >>> import torch
      >>> from torchmetrics.audio import SourceAggregatedSignalDistortionRatio
      >>> metric = SourceAggregatedSignalDistortionRatio()
      >>> metric.update(torch.rand(2,8000), torch.rand(2,8000))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.audio import SourceAggregatedSignalDistortionRatio
      >>> metric = SourceAggregatedSignalDistortionRatio()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(2,8000), torch.rand(2,8000)))
      >>> fig_, ax_ = metric.plot(values)
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code: def plot(
      self,
      val: Union[Tensor, Sequence[Tensor], None] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting a single value
          >>> import torch
          >>> from torchmetrics.audio import SourceAggregatedSignalDistortionRatio
          >>> metric = SourceAggregatedSignalDistortionRatio()
          >>> metric.update(torch.rand(2,8000), torch.rand(2,8000))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.audio import SourceAggregatedSignalDistortionRatio
          >>> metric = SourceAggregatedSignalDistortionRatio()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(2,8000), torch.rand(2,8000)))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
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 4 for calling the SourceAggregatedSignalDistortionRatio obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self