class_docstr: Sequential RNNLM.
  
  See also:
      https://github.com/pytorch/examples/blob/4581968193699de14b56527296262dd76ab43557/word_language_model/model.py
class_docstr_tok: ["Sequential","RNNLM",".","See","also",":","https",":","\/\/github.com\/pytorch\/examples\/blob\/4581968193699de14b56527296262dd76ab43557\/word_language_model\/model.py"]
docstr: Initialize class.
  
  Args:
      n_vocab (int): The size of the vocabulary
      args (argparse.Namespace): configurations. see py:method:`add_arguments`
docstr_tok: ["Initialize","class",".","Args",":","n_vocab","(","int",")",":","The","size","of","the","vocabulary","args","(","argparse.Namespace",")",":","configurations",".","see","py",":","method",":","`","add_arguments","`"]
code: def __init__(self, n_vocab, args):
      """Initialize class.
  
      Args:
          n_vocab (int): The size of the vocabulary
          args (argparse.Namespace): configurations. see py:method:`add_arguments`
  
      """
      torch.nn.Module.__init__(self)
      self._setup(
          rnn_type=args.type.upper(),
          ntoken=n_vocab,
          ninp=args.unit,
          nhid=args.unit,
          nlayers=args.layer,
          dropout=args.dropout_rate,
      )
  
file_path: espnet/espnet/nets/pytorch_backend/lm/seq_rnn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from espnet.nets.lm_interface import LMInterface
label_desc: Use image node_id 2 to create a new SequentialRNNLM object from inherited base classes: LMInterface, torch.nn with example: obj = SequentialRNNLM(n_vocab, args)