code: def __init__(
      self,
      sample_rate,
      win_size,
      hop_size,
      n_fft,
      n_mels,
      f_min,
      f_max,
      window_fn,
      spec_bwd_max_iter=32,
      fp16=False,
  ):
      super().__init__()
      self.inv_mel_transform = PseudoInverseMelScale(
          n_stft=n_fft // 2 + 1,
          n_mels=n_mels,
          sample_rate=sample_rate,
          f_min=f_min,
          f_max=f_max,
      )
      self.gl_transform = GriffinLim(
          n_fft=n_fft,
          win_length=win_size,
          hop_length=hop_size,
          window_fn=window_fn,
          n_iter=spec_bwd_max_iter,
      )
      if fp16:
          self.half()
          self.inv_mel_transform.half()
          self.gl_transform.half()
      else:
          self.float()
          self.inv_mel_transform.float()
          self.gl_transform.float()
  
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 GriffinLimVocoder object from inherited base classes: nn with example: obj = GriffinLimVocoder(sample_rate, win_size, hop_size, n_fft, n_mels, f_min, f_max, window_fn, spec_bwd_max_iter, fp16)