class_docstr: Unsupervised ASR model.
  
  The source code is from FAIRSEQ:
  https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec/unsupervised
code: def encode(
      self, speech: torch.Tensor, speech_lengths: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      with autocast(False):
          # 1. Extract feats
          feats, feats_lengths = self._extract_feats(
              speech, speech_lengths
          )
      padding_mask = make_pad_mask(feats_lengths).to(feats.device)
  
      # 2. Apply feats
      if self.use_segmenter:
          feats, padding_mask = self.segmenter.pre_segment(
              feats, padding_mask
          )
  
      return feats, padding_mask
  
imports: import argparse
  import logging
  from contextlib import contextmanager
  from typing import Dict, Optional, Tuple
  import editdistance
  import torch
  import torch.nn.functional
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.text.token_id_converter import TokenIDConverter
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet2.uasr.discriminator.abs_discriminator import AbsDiscriminator
  from espnet2.uasr.generator.abs_generator import AbsGenerator
  from espnet2.uasr.loss.abs_loss import AbsUASRLoss
  from espnet2.uasr.segmenter.abs_segmenter import AbsSegmenter
  from espnet2.utils.types import str2bool
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
label_desc: Use image node_id 8 for calling the ESPnetUASRModel obj's underlying member method code with example usage: obj.encode(speech, speech_lengths) and returns: feats,  padding_mask