class_docstr: CTC-attention hybrid Encoder-Decoder model
docstr: Frontend + Encoder. Note that this method is used by st_inference.py
  
  Args:
      speech: (Batch, Length, ...)
      speech_lengths: (Batch, )
docstr_tok: ["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_int_enc: bool = False,
  ) -> 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.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)
      encoder_out, encoder_out_lens, _ = self.encoder(
          feats, feats_lengths
      )
  
      if return_int_enc:
          int_encoder_out, int_encoder_out_lens = (
              encoder_out,
              encoder_out_lens,
          )
  
      if self.hier_encoder is not None:
          encoder_out, encoder_out_lens, _ = self.hier_encoder(
              encoder_out, encoder_out_lens
          )
  
      # 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_int_enc:
          return (
              encoder_out,
              encoder_out_lens,
              int_encoder_out,
              int_encoder_out_lens,
          )
      return encoder_out, encoder_out_lens
  
imports: import logging
  import random
  from contextlib import contextmanager
  from itertools import groupby
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from packaging.version import parse
  from torch.nn.utils.rnn import pad_sequence
  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.asr_transducer.utils import get_transducer_task_io
  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.e2e_mt_common import ErrorCalculator
  from espnet.nets.pytorch_backend.nets_utils import th_accuracy
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 4 for calling the ESPnetSTModel obj's underlying member method code with example usage: obj.encode(speech, speech_lengths, return_int_enc) and returns: encoder_out,  encoder_out_lens, encoder_out,  encoder_out_lens,  int_encoder_out,  int_encoder_out_lens