class_docstr: CTC-attention hybrid Encoder-Decoder model
class_docstr_tok: ["CTC-attention","hybrid","Encoder-Decoder","model"]
docstr: Frontend + Encoder. Note that this method is used by s2t_inference.py
  
  Args:
      speech: (Batch, Length, ...)
      speech_lengths: (Batch, )
docstr_tok: ["Frontend","+","Encoder",".","Note","that","this","method","is","used","by","s2t_inference.py","Args",":","speech",":","(","Batch",",","Length",",","...",")","speech_lengths",":","(","Batch",",",")"]
code: def encode(
      self, speech: torch.Tensor, speech_lengths: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Frontend + Encoder. Note that this method is used by s2t_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.normalize is not None:
              feats, feats_lengths = self.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)
      if self.encoder.interctc_use_conditioning:
          encoder_out, encoder_out_lens, _ = self.encoder(
              feats, feats_lengths, ctc=self.ctc
          )
      else:
          encoder_out, encoder_out_lens, _ = self.encoder(
              feats, feats_lengths
          )
      intermediate_outs = None
      if isinstance(encoder_out, tuple):
          intermediate_outs = encoder_out[1]
          encoder_out = encoder_out[0]
  
      # 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),
      )
      if (
          getattr(self.encoder, "selfattention_layer_type", None)
          != "lf_selfattn"
          and not self.is_encoder_whisper
      ):
          assert encoder_out.size(-2) <= encoder_out_lens.max(), (
              encoder_out.size(),
              encoder_out_lens.max(),
          )
  
      if intermediate_outs is not None:
          return (encoder_out, intermediate_outs), encoder_out_lens
  
      return encoder_out, encoder_out_lens
  
imports: import logging
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.cuda.amp import autocast
  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.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.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.pytorch_backend.nets_utils import pad_list, th_accuracy
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 4 for calling the ESPnetS2TModel obj's underlying member method code with example usage: obj.encode(speech, speech_lengths) and returns: encoder_out,  encoder_out_lens, encoder_out,  intermediate_outs,  encoder_out_lens