code: def get_default_train_args(**kwargs):
      train_defaults = dict(
          etype="vggblstmp",
          elayers=1,
          subsample="1_2_2_1_1",
          eunits=4,
          eprojs=4,
          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",
          transducer_loss_weight=1.0,
          use_ctc_loss=False,
          ctc_loss_weight=0.0,
          ctc_loss_dropout_rate=0.0,
          use_lm_loss=False,
          lm_loss_weight=0.0,
          use_aux_transducer_loss=False,
          aux_transducer_loss_weight=0.0,
          aux_transducer_loss_enc_output_layers=[],
          use_symm_kl_div_loss=False,
          symm_kl_div_loss_weight=0.0,
          char_list=["a", "b", "c", "d"],
          sym_space="<space>",
          sym_blank="<blank>",
          report_cer=False,
          report_wer=False,
          verbose=0,
          outdir=None,
          rnnlm=None,
          model_module="espnet.nets.pytorch_backend.e2e_asr_transducer:E2E",
      )
      train_defaults.update(kwargs)
  
      return argparse.Namespace(**train_defaults)
  
imports: import argparse
  import json
  import tempfile
  import numpy
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  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_default_train_args() and returns: argparse