class_docstr: Default RNNLM wrapper to compute reduce framewise loss values.
  
  Args:
      n_vocab (int): The size of the vocabulary
      args (argparse.Namespace): configurations. see `add_arguments`
class_docstr_tok: ["Default","RNNLM","wrapper","to","compute","reduce","framewise","loss","values",".","Args",":","n_vocab","(","int",")",":","The","size","of","the","vocabulary","args","(","argparse.Namespace",")",":","configurations",".","see","`","add_arguments","`"]
code: def add_arguments(parser):
      parser.add_argument(
          "--type",
          type=str,
          default="lstm",
          nargs="?",
          choices=["lstm", "gru"],
          help="Which type of RNN to use",
      )
      parser.add_argument(
          "--layer",
          "-l",
          type=int,
          default=2,
          help="Number of hidden layers",
      )
      parser.add_argument(
          "--unit",
          "-u",
          type=int,
          default=650,
          help="Number of hidden units",
      )
      return parser
  
imports: import copy
  import json
  import logging
  import chainer
  import chainer.functions
  import chainer.links
  import numpy
  from chainer import link, reporter, training
  from chainer.dataset import convert
  from chainer.functions.loss import softmax_cross_entropy
  from chainer.training import extensions
  import espnet.nets.chainer_backend.deterministic_embed_id
  from espnet.lm.lm_utils import MakeSymlinkToBestModel, ParallelSentenceIterator, compute_perplexity, count_tokens, read_tokens
  from espnet.nets.lm_interface import LMInterface
  from espnet.optimizer.factory import dynamic_import_optimizer
  from espnet.scheduler.chainer import ChainerScheduler
  from espnet.scheduler.scheduler import dynamic_import_scheduler
  from espnet.utils.deterministic_utils import set_deterministic_chainer
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
label_desc: Use image node_id 1 for calling the DefaultRNNLM obj's underlying member method code with example usage: obj.add_arguments(parser) and returns: parser