code: def forward(self, specgram: torch.Tensor) -> torch.Tensor:
      angles = np.angle(
          np.exp(2j * np.pi * np.random.rand(*specgram.shape))
      )
      angles = torch.from_numpy(angles).to(specgram)
      _specgram = specgram.view(
          -1, specgram.shape[-2], specgram.shape[-1]
      )
      waveform = self.inverse(_specgram, angles).squeeze(1)
      for _ in range(self.n_iter):
          _, angles = self.transform(waveform)
          waveform = self.inverse(_specgram, angles).squeeze(1)
      return waveform.squeeze(0)
  
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 4 for calling the GriffinLim obj's underlying member method code with example usage: obj.forward(specgram) and returns: waveform