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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code: def __init__(
      self,
      zero_mean: bool = False,
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
      if not isinstance(zero_mean, bool):
          raise ValueError(
              f"Expected argument `zero_mean` to be an bool, but got {zero_mean}"
          )
      self.zero_mean = zero_mean
  
      self.add_state(
          "ci_snr_sum", default=tensor(0.0), dist_reduce_fx="sum"
      )
      self.add_state("num", default=tensor(0), dist_reduce_fx="sum")
  
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 1 to create a new ComplexScaleInvariantSignalNoiseRatio object from inherited base classes: Metric with example: obj = ComplexScaleInvariantSignalNoiseRatio(zero_mean)