code: def get_rnnt_args(**kwargs):
      train_defaults = dict(
          etype="vggblstm",
          elayers=1,
          subsample="1_2_2_1_1",
          eunits=2,
          eprojs=2,
          dtype="lstm",
          dlayers=1,
          dunits=4,
          dec_embed_dim=4,
          dropout_rate=0.0,
          dropout_rate_decoder=0.0,
          dropout_rate_embed_decoder=0.0,
          joint_dim=2,
          joint_activation_type="tanh",
          aux_task_type=None,
          rnnt_mode="rnnt",
          trans_type="warp-transducer",
          char_list=["a", "b", "c", "d"],
          sym_space="<space>",
          sym_blank="<blank>",
          report_cer=False,
          report_wer=False,
          beam_size=1,
          nbest=1,
          verbose=0,
          outdir=None,
          rnnlm=None,
          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_transducer: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 2 for calling a global function with example usage: get_rnnt_args() and returns: argparse