code: def __init__(
      self,
      n_fft: int,
      win_length: int,
      hop_length: int,
      n_iter: int,
      window_fn=torch.hann_window,
  ):
      super(GriffinLim, self).__init__()
      self.transform = TTSSpectrogram(
          n_fft, win_length, hop_length, return_phase=True
      )
  
      basis = get_fourier_basis(n_fft)
      basis = torch.pinverse(n_fft / hop_length * basis).T[:, None, :]
      basis *= get_window(window_fn, n_fft, win_length)
      self.register_buffer("basis", basis)
  
      self.n_fft = n_fft
      self.win_length = win_length
      self.hop_length = hop_length
      self.n_iter = n_iter
  
      self.tiny = 1.1754944e-38
  
imports: import json
  import logging
  from typing import Dict
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
  from fairseq.data.audio.audio_utils import TTSSpectrogram, get_fourier_basis, get_mel_filters, get_window
  from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.models.text_to_speech.codehifigan import CodeGenerator
  from fairseq.models.text_to_speech.hifigan import Generator
  from fairseq.models.text_to_speech.hub_interface import VocoderHubInterface
label_desc: Use image node_id 1 to create a new GriffinLim object from inherited base classes: torch.nn with example: obj = GriffinLim(n_fft, win_length, hop_length, n_iter, window_fn)