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 asr_inference.py
  
  Args:
      speech: (Batch, Length, ...)
      speech_lengths: (Batch, )
docstr_tok: ["Frontend","+","Encoder",".","Note","that","this","method","is","used","by","asr_inference.py","Args",":","speech",":","(","Batch",",","Length",",","...",")","speech_lengths",":","(","Batch",",",")"]
code: def encode(
      self,
      speech: torch.Tensor,
      speech_lengths: torch.Tensor,
      transcript_pad: torch.Tensor = None,
      transcript_pad_lens: torch.Tensor = None,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Frontend + Encoder. Note that this method is used by asr_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
          )
  
      if self.postdecoder is not None:
          if (
              self.encoder._output_size
              != self.postdecoder.output_size_dim
          ):
              encoder_out = self.uniform_linear(encoder_out)
          transcript_list = [
              " ".join(
                  [
                      self.transcript_token_list[int(k)]
                      for k in k1
                      if k != -1
                  ]
              )
              for k1 in transcript_pad
          ]
          (
              transcript_input_id_features,
              transcript_input_mask_features,
              transcript_segment_ids_feature,
              transcript_position_ids_feature,
              input_id_length,
          ) = self.postdecoder.convert_examples_to_features(
              transcript_list, 128
          )
          bert_encoder_out = self.postdecoder(
              torch.LongTensor(transcript_input_id_features).to(
                  device=speech.device
              ),
              torch.LongTensor(transcript_input_mask_features).to(
                  device=speech.device
              ),
              torch.LongTensor(transcript_segment_ids_feature).to(
                  device=speech.device
              ),
              torch.LongTensor(transcript_position_ids_feature).to(
                  device=speech.device
              ),
          )
          bert_encoder_lens = torch.LongTensor(input_id_length).to(
              device=speech.device
          )
          bert_encoder_out = bert_encoder_out[
              :, : torch.max(bert_encoder_lens)
          ]
          final_encoder_out_lens = encoder_out_lens + bert_encoder_lens
          max_lens = torch.max(final_encoder_out_lens)
          encoder_new_out = torch.zeros(
              (encoder_out.shape[0], max_lens, encoder_out.shape[2])
          ).to(device=speech.device)
          for k in range(len(encoder_out)):
              encoder_new_out[k] = torch.cat(
                  (
                      encoder_out[k, : encoder_out_lens[k]],
                      bert_encoder_out[k, : bert_encoder_lens[k]],
                      torch.zeros(
                          (
                              max_lens - final_encoder_out_lens[k],
                              encoder_out.shape[2],
                          )
                      ).to(device=speech.device),
                  ),
                  0,
              )
          if self.deliberationencoder is not None:
              (
                  encoder_new_out,
                  final_encoder_out_lens,
              ) = self.deliberationencoder(
                  encoder_new_out, final_encoder_out_lens
              )
          encoder_out = encoder_new_out
          encoder_out_lens = final_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 intermediate_outs is not None:
          return (encoder_out, intermediate_outs), encoder_out_lens
  
      return encoder_out, encoder_out_lens
  
imports: 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.espnet_model import ESPnetASRModel
  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.error_calculator import ErrorCalculatorTransducer
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.slu.postdecoder.abs_postdecoder import AbsPostDecoder
  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.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 4 for calling the ESPnetSLUModel obj's underlying member method code with example usage: obj.encode(speech, speech_lengths, transcript_pad, transcript_pad_lens) and returns: encoder_out,  encoder_out_lens, encoder_out,  intermediate_outs,  encoder_out_lens