code: def from_data_cfg(cls, args, data_cfg: S2TDataConfig):
      feat_cfg = data_cfg.config["features"]
      window_fn = getattr(torch, feat_cfg["window_fn"] + "_window")
      return cls(
          sample_rate=feat_cfg["sample_rate"],
          win_size=int(feat_cfg["win_len_t"] * feat_cfg["sample_rate"]),
          hop_size=int(feat_cfg["hop_len_t"] * feat_cfg["sample_rate"]),
          n_fft=feat_cfg["n_fft"],
          n_mels=feat_cfg["n_mels"],
          f_min=feat_cfg["f_min"],
          f_max=feat_cfg["f_max"],
          window_fn=window_fn,
          spec_bwd_max_iter=args.spec_bwd_max_iter,
          fp16=args.fp16,
      )
  
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 3 for calling the GriffinLimVocoder obj's underlying member method code with example usage: obj.from_data_cfg(cls, args, data_cfg) and returns: cls