class_docstr: ASV Spoofing model
  A simple ASV Spoofing model
class_docstr_tok: ["ASV","Spoofing","model","A","simple","ASV","Spoofing","model"]
docstr: Frontend + Encoder
  Args:
      speech: (Batch, Length, ...)
      speech_lengths: (Batch,)
      bottleneck_feats: (Batch, Length, ...): used for enh + diar
docstr_tok: ["Frontend","+","Encoder","Args",":","speech",":","(","Batch",",","Length",",","...",")","speech_lengths",":","(","Batch",",",")","bottleneck_feats",":","(","Batch",",","Length",",","...",")",":","used","for","enh","+","diar"]
code: def encode(
      self,
      speech: torch.Tensor,
      speech_lengths: torch.Tensor,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """Frontend + Encoder
      Args:
          speech: (Batch, Length, ...)
          speech_lengths: (Batch,)
          bottleneck_feats: (Batch, Length, ...): used for enh + diar
      """
      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, Dim)
          encoder_out, encoder_out_lens, _ = self.encoder(
              feats, feats_lengths
          )
  
      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(),
      )
  
      return encoder_out, encoder_out_lens
  
imports: import logging
  from contextlib import contextmanager
  from itertools import permutations
  from typing import Dict, Optional, Tuple
  import numpy
  import torch
  import torch.nn.functional
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.asvspoof.decoder.abs_decoder import AbsDecoder
  from espnet2.asvspoof.loss.abs_loss import AbsASVSpoofLoss
  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.pytorch_backend.nets_utils import to_device
label_desc: Use image node_id 4 for calling the ESPnetASVSpoofModel obj's underlying member method code with example usage: obj.encode(speech, speech_lengths) and returns: encoder_out,  encoder_out_lens