class_docstr: VGGRNNEncoder class.
  
  Args:
      input_size: The number of expected features in the input
      bidirectional: If ``True`` becomes a bidirectional LSTM
      use_projection: Use projection layer or not
      num_layers: Number of recurrent layers
      hidden_size: The number of hidden features
      output_size: The number of output features
      dropout: dropout probability
class_docstr_tok: ["VGGRNNEncoder","class",".","Args",":","input_size",":","The","number","of","expected","features","in","the","input","bidirectional",":","If","``","True","``","becomes","a","bidirectional","LSTM","use_projection",":","Use","projection","layer","or","not","num_layers",":","Number","of","recurrent","layers","hidden_size",":","The","number","of","hidden","features","output_size",":","The","number","of","output","features","dropout",":","dropout","probability"]
code: def output_size(self) -> int:
      return self._output_size
  
file_path: espnet/espnet2/asr/encoder/vgg_rnn_encoder.py
imports: from typing import Tuple
  import numpy
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.e2e_asr_common import get_vgg2l_odim
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.rnn.encoders import RNN, RNNP, VGG2L
label_desc: Use image node_id 2 for calling the VGGRNNEncoder obj's underlying member method code with example usage: obj.output_size() and returns: self