class_docstr: Calculate `Complex scale-invariant signal-to-noise ratio`_ (C-SI-SNR) metric for evaluating quality of audio.
  
  As input to `forward` and `update` the metric accepts the following input
  
  - ``preds`` (:class:`~torch.Tensor`): real float tensor with shape ``(...,frequency,time,2)`` or complex float
    tensor with shape ``(..., frequency,time)``
  
  - ``target`` (:class:`~torch.Tensor`): real float tensor with shape ``(...,frequency,time,2)`` or complex float
    tensor with shape ``(..., frequency,time)``
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``c_si_snr`` (:class:`~torch.Tensor`): float scalar tensor with average C-SI-SNR value over samples
  
  Args:
      zero_mean: if to zero mean target and preds or not
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      ValueError:
          If ``zero_mean`` is not an bool
      TypeError:
          If ``preds`` is not the shape (..., frequency, time, 2) (after being converted to real if it is complex).
          If ``preds`` and ``target`` does not have the same shape.
  
  Example:
      >>> import torch
      >>> from torch import tensor
      >>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
      >>> g = torch.manual_seed(1)
      >>> preds = torch.randn((1,257,100,2))
      >>> target = torch.randn((1,257,100,2))
      >>> c_si_snr = ComplexScaleInvariantSignalNoiseRatio()
      >>> c_si_snr(preds, target)
      tensor(-63.4849)
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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 ComplexScaleInvariantSignalNoiseRatio
      >>> metric = ComplexScaleInvariantSignalNoiseRatio()
      >>> metric.update(torch.rand(1,257,100,2), torch.rand(1,257,100,2))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
      >>> metric = ComplexScaleInvariantSignalNoiseRatio()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(1,257,100,2), torch.rand(1,257,100,2)))
      >>> 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 ComplexScaleInvariantSignalNoiseRatio
          >>> metric = ComplexScaleInvariantSignalNoiseRatio()
          >>> metric.update(torch.rand(1,257,100,2), torch.rand(1,257,100,2))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
          >>> metric = ComplexScaleInvariantSignalNoiseRatio()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(1,257,100,2), torch.rand(1,257,100,2)))
          >>> 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.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 4 for calling the ComplexScaleInvariantSignalNoiseRatio obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self