class_docstr: SI-SNR (or named SI-SDR) loss
  
  A more stable SI-SNR loss with clamp from `fast_bss_eval`.
  
  Attributes:
      clamp_db: float
          clamp the output value in  [-clamp_db, clamp_db]
      zero_mean: bool
          When set to True, the mean of all signals is subtracted prior.
      eps: float
          Deprecated. Kept for compatibility.
class_docstr_tok: ["SI-SNR","(","or","named","SI-SDR",")","loss","A","more","stable","SI-SNR","loss","with","clamp","from","`","fast_bss_eval","`",".","Attributes",":","clamp_db",":","float","clamp","the","output","value","in","[","-clamp_db",",","clamp_db","]","zero_mean",":","bool","When","set","to","True",",","the","mean","of","all","signals","is","subtracted","prior",".","eps",":","float","Deprecated",".","Kept","for","compatibility","."]
code: def __init__(
      self,
      clamp_db=None,
      zero_mean=True,
      eps=None,
      name=None,
      only_for_test=False,
      is_noise_loss=False,
      is_dereverb_loss=False,
  ):
      _name = "si_snr_loss" if name is None else name
      super().__init__(
          _name,
          only_for_test=only_for_test,
          is_noise_loss=is_noise_loss,
          is_dereverb_loss=is_dereverb_loss,
      )
  
      self.clamp_db = clamp_db
      self.zero_mean = zero_mean
      if eps is not None:
          logging.warning(
              "Eps is deprecated in si_snr loss, set clamp_db instead."
          )
          if self.clamp_db is None:
              self.clamp_db = -math.log10(eps / (1 - eps)) * 10
  
imports: import logging
  import math
  from abc import ABC
  import ci_sdr
  import fast_bss_eval
  import torch
  from packaging.version import parse
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.loss.criterions.abs_loss import AbsEnhLoss
  from espnet2.layers.stft import Stft
label_desc: Use image node_id 1 to create a new SISNRLoss object from inherited base classes: TimeDomainLoss with example: obj = SISNRLoss(clamp_db, zero_mean, eps, name, only_for_test, is_noise_loss, is_dereverb_loss)