code: def get_rnn_args(**kwargs):
      train_defaults = dict(
          elayers=1,
          subsample="1_2_2_1_1",
          etype="vggblstm",
          eunits=2,
          eprojs=2,
          dtype="lstm",
          dlayers=1,
          dunits=2,
          atype="location",
          aheads=1,
          awin=2,
          aconv_chans=1,
          aconv_filts=2,
          mtlalpha=1.0,
          lsm_type="",
          lsm_weight=0.0,
          sampling_probability=0.0,
          adim=2,
          dropout_rate=0.0,
          dropout_rate_decoder=0.0,
          nbest=1,
          beam_size=1,
          penalty=0.5,
          maxlenratio=1.0,
          minlenratio=0.0,
          ctc_weight=0.2,
          lm_weight=0.0,
          rnnlm=None,
          verbose=2,
          char_list=["a", "e", "i", "o", "u"],
          outdir=None,
          ctc_type="builtin",
          report_cer=False,
          report_wer=False,
          sym_space="<space>",
          sym_blank="<blank>",
          replace_sos=False,
          tgt_lang=False,
          enc_init=None,
          enc_init_mods="enc.",
          dec_init=None,
          dec_init_mods="dec.",
          freeze_mods=None,
          model_module="espnet.nets.pytorch_backend.e2e_asr:E2E",
      )
      train_defaults.update(kwargs)
  
      return argparse.Namespace(**train_defaults)
  
file_path: espnet/test/test_asr_init.py
imports: import argparse
  import json
  import os
  import tempfile
  import numpy
  import pytest
  import torch
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.asr_utils import torch_save
  from espnet.asr.pytorch_backend.asr_init import freeze_modules, load_trained_modules
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 1 for calling a global function with example usage: get_rnn_args() and returns: argparse