class_docstr: Simple LSTM Decoder for ASR models
  Args:
      feat_in (int): size of the input features
      num_classes (int): the size of the vocabulary
      lstm_hidden_size (int): hidden size of the LSTM layers
      vocabulary (vocab): The vocabulary
      bidirectional (bool): default is False. Whether LSTMs are bidirectional or not
      num_layers (int): default is 1. Number of LSTM layers stacked
class_docstr_tok: ["Simple","LSTM","Decoder","for","ASR","models","Args",":","feat_in","(","int",")",":","size","of","the","input","features","num_classes","(","int",")",":","the","size","of","the","vocabulary","lstm_hidden_size","(","int",")",":","hidden","size","of","the","LSTM","layers","vocabulary","(","vocab",")",":","The","vocabulary","bidirectional","(","bool",")",":","default","is","False",".","Whether","LSTMs","are","bidirectional","or","not","num_layers","(","int",")",":","default","is","1",".","Number","of","LSTM","layers","stacked"]
code: def output_types(self):
      return OrderedDict(
          {"logprobs": NeuralType(("B", "T", "D"), LogprobsType())}
      )
  
file_path: NeMo/nemo/collections/asr/modules/lstm_decoder.py
imports: from collections import OrderedDict
  import torch
  import torch.nn
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.exportable import Exportable
  from nemo.core.classes.module import NeuralModule
  from nemo.core.neural_types import AcousticEncodedRepresentation, LogprobsType, NeuralType
label_desc: Use image node_id 2 for calling the LSTMDecoder obj's underlying member method code with example usage: obj.output_types() and returns: OrderedDict