class_docstr: Linear encoder module.
  
  Args:
      input_size: input dim
      output_size: dimension of attention
      linear_units: the number of units of position-wise feed forward
      dropout_rate: dropout rate
      input_layer: input layer type
      normalize_before: whether to use layer_norm before the first block
      padding_idx: padding_idx for input_layer=embed
code: def output_size(self) -> int:
      return self._output_size
  
file_path: espnet/espnet2/asr/encoder/linear_encoder.py
imports: from typing import List, Optional, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
  from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling, Conv2dSubsampling2, Conv2dSubsampling6, Conv2dSubsampling8, TooShortUttError, check_short_utt
label_desc: Use image node_id 2 for calling the LinearEncoder obj's underlying member method code with example usage: obj.output_size() and returns: self