class_docstr: Conformer encoder module.
  
  Args:
      input_size (int): Input dimension.
      output_size (int): Dimension of attention.
      attention_heads (int): The number of heads of multi head attention.
      linear_units (int): The number of units of position-wise feed forward.
      num_blocks (int): The number of decoder blocks.
      dropout_rate (float): Dropout rate.
      attention_dropout_rate (float): Dropout rate in attention.
      positional_dropout_rate (float): Dropout rate after adding positional encoding.
      input_layer (Union[str, torch.nn.Module]): Input layer type.
      normalize_before (bool): Whether to use layer_norm before the first block.
      concat_after (bool): Whether to concat attention layer's input and output.
          If True, additional linear will be applied.
          i.e. x -> x + linear(concat(x, att(x)))
          If False, no additional linear will be applied. i.e. x -> x + att(x)
      positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
      positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
      rel_pos_type (str): Whether to use the latest relative positional encoding or
          the legacy one. The legacy relative positional encoding will be deprecated
          in the future. More Details can be found in
          https://github.com/espnet/espnet/pull/2816.
      encoder_pos_enc_layer_type (str): Encoder positional encoding layer type.
      encoder_attn_layer_type (str): Encoder attention layer type.
      activation_type (str): Encoder activation function type.
      macaron_style (bool): Whether to use macaron style for positionwise layer.
      use_cnn_module (bool): Whether to use convolution module.
      zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
      cnn_module_kernel (int): Kernerl size of convolution module.
      padding_idx (int): Padding idx for input_layer=embed.
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code: def output_size(self) -> int:
      return self._output_size
  
imports: import logging
  from typing import List, Optional, Tuple, Union
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.ctc import CTC
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.pytorch_backend.conformer.convolution import ConvolutionModule
  from espnet.nets.pytorch_backend.conformer.encoder_layer import EncoderLayer
  from espnet.nets.pytorch_backend.nets_utils import get_activation, make_pad_mask, trim_by_ctc_posterior
  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.multi_layer_conv import Conv1dLinear, MultiLayeredConv1d
  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 Conv2dSubsampling, Conv2dSubsampling1, Conv2dSubsampling2, Conv2dSubsampling6, Conv2dSubsampling8, TooShortUttError, check_short_utt
label_desc: Use image node_id 2 for calling the ConformerEncoder obj's underlying member method code with example usage: obj.output_size() and returns: self