code: def griffinlim(
      specgram: Tensor,
      window: Tensor,
      n_fft: int,
      hop_length: int,
      win_length: int,
      power: float,
      n_iter: int,
      momentum: float,
      length: Optional[int],
      rand_init: bool,
  ) -> Tensor:
      r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.
  
      .. devices:: CPU CUDA
  
      .. properties:: Autograd TorchScript
  
      Implementation ported from
      *librosa* :cite:`brian_mcfee-proc-scipy-2015`, *A fast Griffin-Lim algorithm* :cite:`6701851`
      and *Signal estimation from modified short-time Fourier transform* :cite:`1172092`.
  
      Args:
          specgram (Tensor): A magnitude-only STFT spectrogram of dimension `(..., freq, frames)`
              where freq is ``n_fft // 2 + 1``.
          window (Tensor): Window tensor that is applied/multiplied to each frame/window
          n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins
          hop_length (int): Length of hop between STFT windows. (
              Default: ``win_length // 2``)
          win_length (int): Window size. (Default: ``n_fft``)
          power (float): Exponent for the magnitude spectrogram,
              (must be > 0) e.g., 1 for magnitude, 2 for power, etc.
          n_iter (int): Number of iteration for phase recovery process.
          momentum (float): The momentum parameter for fast Griffin-Lim.
              Setting this to 0 recovers the original Griffin-Lim method.
              Values near 1 can lead to faster convergence, but above 1 may not converge.
          length (int or None): Array length of the expected output.
          rand_init (bool): Initializes phase randomly if True, to zero otherwise.
  
      Returns:
          Tensor: waveform of `(..., time)`, where time equals the ``length`` parameter if given.
      """
      if not 0 <= momentum < 1:
          raise ValueError(
              "momentum must be in range [0, 1). Found: {}".format(
                  momentum
              )
          )
  
      momentum = momentum / (1 + momentum)
  
      # pack batch
      shape = specgram.size()
      specgram = specgram.reshape([-1] + list(shape[-2:]))
  
      specgram = specgram.pow(1 / power)
  
      # initialize the phase
      if rand_init:
          angles = torch.rand(
              specgram.size(),
              dtype=_get_complex_dtype(specgram.dtype),
              device=specgram.device,
          )
      else:
          angles = torch.full(
              specgram.size(),
              1,
              dtype=_get_complex_dtype(specgram.dtype),
              device=specgram.device,
          )
  
      # And initialize the previous iterate to 0
      tprev = torch.tensor(
          0.0, dtype=specgram.dtype, device=specgram.device
      )
      for _ in range(n_iter):
          # Invert with our current estimate of the phases
          inverse = torch.istft(
              specgram * angles,
              n_fft=n_fft,
              hop_length=hop_length,
              win_length=win_length,
              window=window,
              length=length,
          )
  
          # Rebuild the spectrogram
          rebuilt = torch.stft(
              input=inverse,
              n_fft=n_fft,
              hop_length=hop_length,
              win_length=win_length,
              window=window,
              center=True,
              pad_mode="reflect",
              normalized=False,
              onesided=True,
              return_complex=True,
          )
  
          # Update our phase estimates
          angles = rebuilt
          if momentum:
              angles = angles - tprev.mul_(momentum)
          angles = angles.div(angles.abs().add(1e-16))
  
          # Store the previous iterate
          tprev = rebuilt
  
      # Return the final phase estimates
      waveform = torch.istft(
          specgram * angles,
          n_fft=n_fft,
          hop_length=hop_length,
          win_length=win_length,
          window=window,
          length=length,
      )
  
      # unpack batch
      waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:])
  
      return waveform
  
imports: import math
  import tempfile
  import warnings
  from collections.abc import Sequence
  from typing import List, Optional, Tuple, Union
  import torch
  import torchaudio
  from torch import Tensor
  from torchaudio._internal.module_utils import deprecated
  from .filtering import highpass_biquad, treble_biquad
label_desc: Use image node_id 5 for calling a global function with example usage: griffinlim(specgram, window, n_fft, hop_length, win_length, power, n_iter, momentum, length, rand_init) and returns: waveform