class_docstr: Speech Pretrained Representation frontend structure for ASR.
docstr: Tile up the representations by `tile_factor`.
  
  Input - sequence of representations
          shape: (batch_size, seq_len, feature_dim)
  
  Output - sequence of tiled representations
           shape: (batch_size, seq_len * factor, feature_dim)
docstr_tok: ["Tile","up","the","representations","by","`","tile_factor","`",".","Input","-","sequence","of","representations","shape",":","(","batch_size",",","seq_len",",","feature_dim",")","Output","-","sequence","of","tiled","representations","shape",":","(","batch_size",",","seq_len","*","factor",",","feature_dim",")"]
code: def _tile_representations(self, feature):
      """Tile up the representations by `tile_factor`.
  
      Input - sequence of representations
              shape: (batch_size, seq_len, feature_dim)
  
      Output - sequence of tiled representations
               shape: (batch_size, seq_len * factor, feature_dim)
      """
      assert (
          len(feature.shape) == 3
      ), "Input argument `feature` has invalid shape: {}".format(
          feature.shape
      )
      tiled_feature = feature.repeat(1, 1, self.tile_factor)
      tiled_feature = tiled_feature.reshape(
          feature.size(0),
          feature.size(1) * self.tile_factor,
          feature.size(2),
      )
      return tiled_feature
  
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 2 for calling the S3prlFrontend obj's underlying member method code with example usage: obj._tile_representations(feature) and returns: tiled_feature