class_docstr: STFT losses.
  
  Args:
      n_fft (int): Size of FFT.
      hop_length (int): Hop length.
      win_length (int): Window length.
      window (str): Window function type.
      normalized (bool): Whether to use normalized STFT or not.
      epsilon (float): Epsilon for numerical stability.
code: def __init__(
      self,
      n_fft: int = 1024,
      hop_length: int = 120,
      win_length: int = 600,
      window: str = "hann_window",
      normalized: bool = False,
      epsilon: float = torch.finfo(torch.float32).eps,
  ):
      super().__init__()
      self.n_fft = n_fft
      self.hop_length = hop_length
      self.win_length = win_length
      self.normalized = normalized
      self.register_buffer("window", getattr(torch, window)(win_length))
      self.spectral_convergenge_loss = SpectralConvergenceLoss(epsilon)
      self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss(epsilon)
  
imports: import typing
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new STFTLosses object from inherited base classes: nn with example: obj = STFTLosses(n_fft, hop_length, win_length, window, normalized, epsilon)