class_docstr: A ready-to-use wrapper for Unit HiFiGAN (discrete units -> waveform).
  Arguments
  ---------
  hparams
      Hyperparameters (from HyperPyYAML)
  Example
  -------
  >>> tmpdir_vocoder = getfixture('tmpdir') / "vocoder"
  >>> hifi_gan = UnitHIFIGAN.from_hparams(source="speechbrain/tts-hifigan-unit-hubert-l6-k100-ljspeech", savedir=tmpdir_vocoder)
  >>> codes = torch.randint(0, 99, (100,))
  >>> waveform = hifi_gan.decode_unit(codes)
class_docstr_tok: ["A","ready-to-use","wrapper","for","Unit","HiFiGAN","(","discrete","units","-",">","waveform",")",".","Arguments","--","--","--","--","-","hparams","Hyperparameters","(","from","HyperPyYAML",")","Example","--","--","--","-",">",">",">","tmpdir_vocoder","=","getfixture","(","'tmpdir","'",")","\/","``","vocoder","''",">",">",">","hifi_gan","=","UnitHIFIGAN.from_hparams","(","source=","''","speechbrain\/tts-hifigan-unit-hubert-l6-k100-ljspeech","''",",","savedir=tmpdir_vocoder",")",">",">",">","codes","=","torch.randint","(","0",",","99",",","(","100",",",")",")",">",">",">","waveform","=","hifi_gan.decode_unit","(","codes",")"]
docstr: Decodes the input units
docstr_tok: ["Decodes","the","input","units"]
code: def forward(self, units):
      "Decodes the input units"
      return self.decode_batch(units)
  
imports: import logging
  import torch
  from speechbrain.dataio.dataio import length_to_mask
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 4 for calling the UnitHIFIGAN obj's underlying member method code with example usage: obj.forward(units) and returns: self