code: def from_pretrained(
      cls,
      model_name_or_path,
      checkpoint_file="model.pt",
      data_name_or_path=".",
      config="config.json",
      fp16: bool = False,
      **kwargs,
  ):
      from fairseq import hub_utils
  
      x = hub_utils.from_pretrained(
          model_name_or_path,
          checkpoint_file,
          data_name_or_path,
          archive_map=cls.hub_models(),
          config_yaml=config,
          fp16=fp16,
          is_vocoder=True,
          **kwargs,
      )
  
      with open(f"{x['args']['data']}/{config}") as f:
          vocoder_cfg = json.load(f)
      assert (
          len(x["args"]["model_path"]) == 1
      ), "Too many vocoder models in the input"
  
      vocoder = CodeHiFiGANVocoder(
          x["args"]["model_path"][0], vocoder_cfg
      )
      return VocoderHubInterface(vocoder_cfg, vocoder)
  
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 5 for calling the CodeHiFiGANVocoder obj's underlying member method code with example usage: obj.from_pretrained(cls, model_name_or_path, checkpoint_file, data_name_or_path, config, fp16) and returns: VocoderHubInterface