code: def __init__(
      self,
      n_fft: int = 512,
      win_length: int = None,
      hop_length: int = 128,
      window: Optional[str] = "hann",
      center: bool = True,
      normalized: bool = False,
      onesided: bool = True,
  ):
      assert check_argument_types()
      super().__init__()
      self.n_fft = n_fft
      if win_length is None:
          self.win_length = n_fft
      else:
          self.win_length = win_length
      self.hop_length = hop_length
      self.center = center
      self.normalized = normalized
      self.onesided = onesided
      if window is not None and not hasattr(torch, f"{window}_window"):
          raise ValueError(f"{window} window is not implemented")
      self.window = window
  
file_path: espnet/espnet2/layers/stft.py
imports: from typing import Optional, Tuple, Union
  import librosa
  import numpy
  import torch
  from packaging.version import parse
  from torch_complex.tensor import ComplexTensor
  from typeguard import check_argument_types
  from espnet2.enh.layers.complex_utils import to_complex
  from espnet2.layers.inversible_interface import InversibleInterface
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
label_desc: Use image node_id 1 to create a new Stft object from inherited base classes: torch.nn, InversibleInterface with example: obj = Stft(n_fft, win_length, hop_length, window, center, normalized, onesided)