code: def get_parser():
      """Get Transducer model inference parser."""
  
      parser = config_argparse.ArgumentParser(
          description="ASR Transducer Decoding",
          formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      )
  
      parser.add_argument(
          "--log_level",
          type=lambda x: x.upper(),
          default="INFO",
          choices=(
              "CRITICAL",
              "ERROR",
              "WARNING",
              "INFO",
              "DEBUG",
              "NOTSET",
          ),
          help="The verbose level of logging",
      )
  
      parser.add_argument("--output_dir", type=str, required=True)
      parser.add_argument(
          "--ngpu",
          type=int,
          default=0,
          help="The number of gpus. 0 indicates CPU mode",
      )
      parser.add_argument(
          "--seed", type=int, default=0, help="Random seed"
      )
      parser.add_argument(
          "--dtype",
          default="float32",
          choices=["float16", "float32", "float64"],
          help="Data type",
      )
      parser.add_argument(
          "--num_workers",
          type=int,
          default=1,
          help="The number of workers used for DataLoader",
      )
  
      group = parser.add_argument_group("Input data related")
      group.add_argument(
          "--data_path_and_name_and_type",
          type=str2triple_str,
          required=True,
          action="append",
      )
      group.add_argument("--key_file", type=str_or_none)
      group.add_argument(
          "--allow_variable_data_keys", type=str2bool, default=False
      )
  
      group = parser.add_argument_group(
          "The model configuration related"
      )
      group.add_argument(
          "--asr_train_config",
          type=str,
          help="ASR training configuration",
      )
      group.add_argument(
          "--asr_model_file",
          type=str,
          help="ASR model parameter file",
      )
      group.add_argument(
          "--lm_train_config",
          type=str,
          help="LM training configuration",
      )
      group.add_argument(
          "--lm_file",
          type=str,
          help="LM parameter file",
      )
      group.add_argument(
          "--model_tag",
          type=str,
          help="Pretrained model tag. If specify this option, *_train_config and "
          "*_file will be overwritten",
      )
  
      group = parser.add_argument_group("Beam-search related")
      group.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="The batch size for inference",
      )
      group.add_argument(
          "--nbest",
          type=int,
          default=1,
          help="Output N-best hypotheses",
      )
      group.add_argument(
          "--beam_size", type=int, default=5, help="Beam size"
      )
      group.add_argument(
          "--lm_weight", type=float, default=1.0, help="RNNLM weight"
      )
      group.add_argument(
          "--beam_search_config",
          default={},
          help="The keyword arguments for transducer beam search.",
      )
  
      group = parser.add_argument_group("Text converter related")
      group.add_argument(
          "--token_type",
          type=str_or_none,
          default=None,
          choices=["char", "bpe", None],
          help="The token type for ASR model. "
          "If not given, refers from the training args",
      )
      group.add_argument(
          "--bpemodel",
          type=str_or_none,
          default=None,
          help="The model path of sentencepiece. "
          "If not given, refers from the training args",
      )
  
      group = parser.add_argument_group("Dynamic quantization related")
      parser.add_argument(
          "--quantize_asr_model",
          type=bool,
          default=False,
          help="Apply dynamic quantization to ASR model.",
      )
      parser.add_argument(
          "--quantize_modules",
          nargs="*",
          default=None,
          help="""Module names to apply dynamic quantization on.
          The module names are provided as a list, where each name is separated
          by a comma (e.g.: --quantize-config=[Linear,LSTM,GRU]).
          Each specified name should be an attribute of 'torch.nn', e.g.:
          torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""",
      )
      parser.add_argument(
          "--quantize_dtype",
          type=str,
          default="qint8",
          choices=["float16", "qint8"],
          help="Dtype for dynamic quantization.",
      )
  
      group = parser.add_argument_group("Streaming related")
      parser.add_argument(
          "--streaming",
          type=bool,
          default=False,
          help="Whether to perform chunk-by-chunk inference.",
      )
      parser.add_argument(
          "--decoding_window",
          type=int,
          default=640,
          help="Audio length (in milliseconds) to process during decoding.",
      )
      parser.add_argument(
          "--left_context",
          type=int,
          default=32,
          help="""Number of previous frames (AFTER subsamplingà the attention module
          can see in current chunk (used by Conformer and Branchformer block).""",
      )
      parser.add_argument(
          "--display_hypotheses",
          type=bool,
          default=False,
          help="""Whether to display hypotheses during inference. If streaming=True,
          partial hypotheses will also be shown.""",
      )
  
      return parser
  
imports: from __future__ import annotations
  import argparse
  import logging
  import sys
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types, check_return_type
  from espnet2.asr_transducer.beam_search_transducer import BeamSearchTransducer, Hypothesis
  from espnet2.asr_transducer.frontend.online_audio_processor import OnlineAudioProcessor
  from espnet2.asr_transducer.utils import TooShortUttError
  from espnet2.fileio.datadir_writer import DatadirWriter
  from espnet2.tasks.asr_transducer import ASRTransducerTask
  from espnet2.tasks.lm import LMTask
  from espnet2.text.build_tokenizer import build_tokenizer
  from espnet2.text.token_id_converter import TokenIDConverter
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.utils import config_argparse
  from espnet2.utils.types import str2bool, str2triple_str, str_or_none
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 2 for calling a global function with example usage: get_parser() and returns: parser