class_docstr: ESPnet speech-to-speech translation model
docstr: Frontend + Encoder. Note that this method is used by st_inference.py
  
  Args:
      speech: (Batch, Length, ...)
      speech_lengths: (Batch, )
code: def encode(
      self,
      speech: torch.Tensor,
      speech_lengths: torch.Tensor,
      return_all_hs: bool = False,
      **kwargs,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Frontend + Encoder. Note that this method is used by st_inference.py
  
      Args:
          speech: (Batch, Length, ...)
          speech_lengths: (Batch, )
      """
      with autocast(False):
          # 1. Extract feats
          feats, feats_lengths = self._extract_feats(
              speech, speech_lengths
          )
  
          # 2. Data augmentation
          if self.specaug is not None and self.training:
              feats, feats_lengths = self.specaug(feats, feats_lengths)
  
          # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
          if self.src_normalize is not None:
              feats, feats_lengths = self.src_normalize(
                  feats, feats_lengths
              )
  
      # Pre-encoder, e.g. used for raw input data
      if self.preencoder is not None:
          feats, feats_lengths = self.preencoder(feats, feats_lengths)
  
      # 4. Forward encoder
      # feats: (Batch, Length, Dim)
      # -> encoder_out: (Batch, Length2, Dim2)
      encoder_out, encoder_out_lens, _ = self.encoder(
          feats, feats_lengths, return_all_hs=return_all_hs
      )
      if return_all_hs:
          encoder_out, inter_encoder_out = encoder_out
  
      # Post-encoder, e.g. NLU
      if self.postencoder is not None:
          encoder_out, encoder_out_lens = self.postencoder(
              encoder_out, encoder_out_lens
          )
  
      assert encoder_out.size(0) == speech.size(0), (
          encoder_out.size(),
          speech.size(0),
      )
      assert encoder_out.size(1) <= encoder_out_lens.max(), (
          encoder_out.size(),
          encoder_out_lens.max(),
      )
  
      if return_all_hs:
          return (encoder_out, inter_encoder_out), encoder_out_lens
      else:
          return encoder_out, encoder_out_lens
  
imports: import logging
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
  from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.postencoder.abs_postencoder import AbsPostEncoder
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.s2st.aux_attention.abs_aux_attention import AbsS2STAuxAttention
  from espnet2.s2st.losses.abs_loss import AbsS2STLoss
  from espnet2.s2st.synthesizer.abs_synthesizer import AbsSynthesizer
  from espnet2.s2st.tgt_feats_extract.abs_tgt_feats_extract import AbsTgtFeatsExtract
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet.nets.e2e_asr_common import ErrorCalculator
  from espnet.nets.e2e_mt_common import ErrorCalculator
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask, th_accuracy
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
label_desc: Use image node_id 5 for calling the ESPnetS2STModel obj's underlying member method code with example usage: obj.encode(speech, speech_lengths, return_all_hs) and returns: encoder_out,  inter_encoder_out,  encoder_out_lens, encoder_out,  encoder_out_lens