code: def forward(
      self, x: Dict[str, torch.Tensor], dur_prediction=False
  ) -> torch.Tensor:
      assert "code" in x
      x["dur_prediction"] = dur_prediction
  
      # remove invalid code
      mask = x["code"] >= 0
      x["code"] = x["code"][mask].unsqueeze(dim=0)
      if "f0" in x:
          f0_up_ratio = x["f0"].size(1) // x["code"].size(1)
          mask = (
              mask.unsqueeze(2)
              .repeat(1, 1, f0_up_ratio)
              .view(-1, x["f0"].size(1))
          )
          x["f0"] = x["f0"][mask].unsqueeze(dim=0)
  
      return self.model(**x).detach().squeeze()
  
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 2 for calling the CodeHiFiGANVocoder obj's underlying member method code with example usage: obj.forward(x, dur_prediction) and returns: self