class_docstr: Speech Pretrained Representation frontend structure for ASR.
code: def forward(
      self, input: torch.Tensor, input_lengths: torch.Tensor
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      feats, feats_lens = self.upstream(input, input_lengths)
      if self.layer != -1:
          layer = self.layer
          feats, feats_lens = feats[layer], feats_lens[layer]
          return feats, feats_lens
  
      if self.multilayer_feature:
          feats, feats_lens = self.featurizer(feats, feats_lens)
      else:
          feats, feats_lens = self.featurizer(
              feats[-1:], feats_lens[-1:]
          )
  
      if self.tile_factor != 1:
          feats = self._tile_representations(feats)
  
      return feats, feats_lens
  
file_path: espnet/espnet2/asr/frontend/s3prl.py
imports: import copy
  import logging
  from typing import Optional, Tuple, Union
  import humanfriendly
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.utils.get_default_kwargs import get_default_kwargs
  from espnet.nets.pytorch_backend.frontends.frontend import Frontend
label_desc: Use image node_id 4 for calling the S3prlFrontend obj's underlying member method code with example usage: obj.forward(input, input_lengths) and returns: feats,  feats_lens, feats,  feats_lens