class_docstr: E-Branchformer encoder module.
class_docstr_tok: ["E-Branchformer","encoder","module","."]
code: def output_size(self) -> int:
      return self._output_size
  
imports: import logging
  from typing import List, Optional, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.ctc import CTC
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.layers.cgmlp import ConvolutionalGatingMLP
  from espnet2.asr.layers.fastformer import FastSelfAttention
  from espnet.nets.pytorch_backend.nets_utils import get_activation, make_pad_mask
  from espnet.nets.pytorch_backend.transformer.attention import LegacyRelPositionMultiHeadedAttention, MultiHeadedAttention, RelPositionMultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.embedding import LegacyRelPositionalEncoding, PositionalEncoding, RelPositionalEncoding, ScaledPositionalEncoding
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
  from espnet.nets.pytorch_backend.transformer.repeat import repeat
  from espnet.nets.pytorch_backend.transformer.subsampling import Conv1dSubsampling1, Conv1dSubsampling2, Conv1dSubsampling3, Conv2dSubsampling, Conv2dSubsampling1, Conv2dSubsampling2, Conv2dSubsampling6, Conv2dSubsampling8, TooShortUttError, check_short_utt
label_desc: Use image node_id 2 for calling the EBranchformerEncoder obj's underlying member method code with example usage: obj.output_size() and returns: self