code: def get_parser():
      parser = config_argparse.ArgumentParser(
          description="speaker embedding extraction",
          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",
      )
      _batch_type_help = ""
      for key, value in BATCH_TYPES.items():
          _batch_type_help += f'"{key}":\n{value}\n'
      group.add_argument(
          "--batch_type",
          type=str,
          default="folded",
          choices=list(BATCH_TYPES),
          help=_batch_type_help,
      )
      group.add_argument(
          "--batch_bins",
          type=int,
          default=1000000,
          help="The number of batch bins. Used if batch_type='length' or 'numel'",
      )
      group.add_argument(
          "--valid_batch_bins",
          type=int_or_none,
          default=None,
          help="If not given, the value of --batch_bins is used",
      )
      group.add_argument(
          "--valid_batch_type",
          type=str_or_none,
          default=None,
          choices=list(BATCH_TYPES) + [None],
          help="If not given, the value of --batch_type is used",
      )
      group.add_argument(
          "--max_cache_size",
          type=humanfriendly.parse_size,
          default=0.0,
          help="The maximum cache size for data loader. e.g. 10MB, 20GB.",
      )
      group.add_argument(
          "--max_cache_fd",
          type=int,
          default=32,
          help="The maximum number of file descriptors to be kept "
          "as opened for ark files. "
          "This feature is only valid when data type is 'kaldi_ark'.",
      )
      group.add_argument(
          "--allow_multi_rates",
          type=str2bool,
          default=False,
          help="Whether to allow audios to have different sampling rates",
      )
      group.add_argument(
          "--valid_max_cache_size",
          type=humanfriendly_parse_size_or_none,
          default=None,
          help="The maximum cache size for validation data loader. e.g. 10MB, 20GB. "
          "If None, the 5 percent size of --max_cache_size",
      )
      group.add_argument(
          "--shape_file", type=str, action="append", default=[]
      )
      group.add_argument(
          "--input_size",
          type=int_or_none,
          default=None,
          help="The number of input dimension of the feature",
      )
      group.add_argument(
          "--num_cohort_spk",
          type=int,
          default=5994,
          help="The number of cohort speakers in score norm",
      )
      group.add_argument(
          "--num_utt_per_spk",
          type=int,
          default=10,
          help="The number of utterances per speaker in score norm",
      )
      group.add_argument(
          "--utt_select_sec",
          type=int,
          default=8,
          help="Minimum duration for including the utt in cohort set in score norm",
      )
      group.add_argument(
          "--average_spk",
          type=str2bool,
          default=False,
          help="whether to average cohort embeds per speaker in score norm",
      )
      group.add_argument(
          "--adaptive_cohort_size",
          type=int,
          default=400,
          help="top-k cohort size in score norm",
      )
      group.add_argument(
          "--qmf_dur_thresh",
          type=int,
          default=6,
          help="threshold of duration to be considered as long in qmf trainset",
      )
      group.add_argument(
          "--qmf_num_trial_per_condition",
          type=int,
          default=5000,
          help="number of trials per condition in qmf trainset",
      )
      group.add_argument(
          "--allow_variable_data_keys", type=str2bool, default=False
      )
      group.add_argument("--average_embd", type=str2bool, default=False)
      group.add_argument(
          "--train_dtype",
          default="float32",
          choices=["float16", "float32", "float64"],
          help="Data type for training.",
      )
      group.add_argument(
          "--use_amp",
          type=str2bool,
          default=False,
          help="Enable Automatic Mixed Precision. This feature requires pytorch>=1.6",
      )
      group.add_argument(
          "--no_forward_run",
          type=str2bool,
          default=False,
          help="Just only iterating data loading without "
          "model forwarding and training",
      )
      group.add_argument(
          "--sort_in_batch",
          type=str,
          default="descending",
          choices=["descending", "ascending"],
          help="Sort the samples in each mini-batches by the sample "
          'lengths. To enable this, "shape_file" must have the length information.',
      )
      group.add_argument(
          "--sort_batch",
          type=str,
          default="descending",
          choices=["descending", "ascending"],
          help="Sort mini-batches by the sample lengths",
      )
      group.add_argument(
          "--drop_last_iter",
          type=str2bool,
          default=False,
          help="Exclude the minibatch with leftovers.",
      )
  
      group = parser.add_argument_group(
          "The model configuration related"
      )
      group.add_argument(
          "--spk_train_config",
          type=str,
          help="SPK training configuration",
      )
      group.add_argument(
          "--spk_model_file",
          type=str,
          help="SPK model 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("distributed training related")
      group.add_argument(
          "--dist_backend",
          default="nccl",
          type=str,
          help="distributed backend",
      )
      group.add_argument(
          "--dist_init_method",
          type=str,
          default="env://",
          help='if init_method="env://", env values of "MASTER_PORT", "MASTER_ADDR", '
          '"WORLD_SIZE", and "RANK" are referred.',
      )
      group.add_argument(
          "--dist_world_size",
          default=None,
          type=int_or_none,
          help="number of nodes for distributed training",
      )
      group.add_argument(
          "--dist_rank",
          type=int_or_none,
          default=None,
          help="node rank for distributed training",
      )
      group.add_argument(
          # Not starting with "dist_" for compatibility to launch.py
          "--local_rank",
          type=int_or_none,
          default=None,
          help="local rank for distributed training. This option is used if "
          "--multiprocessing_distributed=false",
      )
      group.add_argument(
          "--dist_master_addr",
          default=None,
          type=str_or_none,
          help="The master address for distributed training. "
          "This value is used when dist_init_method == 'env://'",
      )
      group.add_argument(
          "--dist_master_port",
          default=None,
          type=int_or_none,
          help="The master port for distributed training"
          "This value is used when dist_init_method == 'env://'",
      )
      group.add_argument(
          "--dist_launcher",
          default=None,
          type=str_or_none,
          choices=["slurm", "mpi", None],
          help="The launcher type for distributed training",
      )
      group.add_argument(
          "--multiprocessing_distributed",
          default=False,
          type=str2bool,
          help="Use multi-processing distributed training to launch "
          "N processes per node, which has N GPUs. This is the "
          "fastest way to use PyTorch for either single node or "
          "multi node data parallel training",
      )
      group.add_argument(
          "--unused_parameters",
          type=str2bool,
          default=False,
          help="Whether to use the find_unused_parameters in "
          "torch.nn.parallel.DistributedDataParallel ",
      )
      group.add_argument(
          "--sharded_ddp",
          default=False,
          type=str2bool,
          help="Enable sharded training provided by fairscale",
      )
  
      group = parser.add_argument_group(
          "trainer initialization related"
      )
      group.add_argument(
          "--use_matplotlib",
          type=str2bool,
          default=True,
          help="Enable matplotlib logging",
      )
      group.add_argument(
          "--use_tensorboard",
          type=str2bool,
          default=True,
          help="Enable tensorboard logging",
      )
      group.add_argument(
          "--create_graph_in_tensorboard",
          type=str2bool,
          default=False,
          help="Whether to create graph in tensorboard",
      )
      group.add_argument(
          "--use_wandb",
          type=str2bool,
          default=False,
          help="Enable wandb logging",
      )
      group.add_argument(
          "--wandb_project",
          type=str,
          default=None,
          help="Specify wandb project",
      )
      group.add_argument(
          "--wandb_id",
          type=str,
          default=None,
          help="Specify wandb id",
      )
      group.add_argument(
          "--wandb_entity",
          type=str,
          default=None,
          help="Specify wandb entity",
      )
      group.add_argument(
          "--wandb_name",
          type=str,
          default=None,
          help="Specify wandb run name",
      )
      group.add_argument(
          "--wandb_model_log_interval",
          type=int,
          default=-1,
          help="Set the model log period",
      )
      group.add_argument(
          "--detect_anomaly",
          type=str2bool,
          default=False,
          help="Set torch.autograd.set_detect_anomaly",
      )
      group.add_argument(
          "--use_lora",
          type=str2bool,
          default=False,
          help="Enable LoRA based finetuning, see (https://arxiv.org/abs/2106.09685) "
          "for large pre-trained foundation models, like Whisper",
      )
      group.add_argument(
          "--save_lora_only",
          type=str2bool,
          default=True,
          help="Only save LoRA parameters or save all model parameters",
      )
      group.add_argument(
          "--lora_conf",
          action=NestedDictAction,
          default=dict(),
          help="Configuration for LoRA based finetuning",
      )
  
      group = parser.add_argument_group("cudnn mode related")
      group.add_argument(
          "--cudnn_enabled",
          type=str2bool,
          default=torch.backends.cudnn.enabled,
          help="Enable CUDNN",
      )
      group.add_argument(
          "--cudnn_benchmark",
          type=str2bool,
          default=torch.backends.cudnn.benchmark,
          help="Enable cudnn-benchmark mode",
      )
      group.add_argument(
          "--cudnn_deterministic",
          type=str2bool,
          default=True,
          help="Enable cudnn-deterministic mode",
      )
  
      group = parser.add_argument_group(
          "The inference hyperparameter related"
      )
      group.add_argument(
          "--valid_batch_size",
          type=int,
          default=1,
          help="The batch size for inference",
      )
      group.add_argument(
          "--target_duration",
          type=float,
          default=3.0,
          help="Duration (in seconds) of samples in a minibatch",
      )
      group.add_argument(
          "--num_eval",
          type=int,
          default=10,
          help="Number of segments to make from one utterance in the inference phase",
      )
      group.add_argument(
          "--fold_length", type=int, action="append", default=[]
      )
      group.add_argument(
          "--use_preprocessor",
          type=str2bool,
          default=True,
          help="Apply preprocessing to data or not",
      )
  
      return parser
  
imports: import argparse
  import logging
  import os
  import sys
  from glob import glob
  import humanfriendly
  import numpy
  import torch
  from torch.multiprocessing.spawn import ProcessContext
  from typeguard import check_argument_types, check_return_type
  from espnet2.samplers.build_batch_sampler import BATCH_TYPES
  from espnet2.tasks.spk import SpeakerTask
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.train.distributed_utils import DistributedOption, free_port, get_master_port, get_node_rank, get_num_nodes, resolve_distributed_mode
  from espnet2.train.reporter import Reporter, SubReporter
  from espnet2.utils import config_argparse
  from espnet2.utils.build_dataclass import build_dataclass
  from espnet2.utils.nested_dict_action import NestedDictAction
  from espnet2.utils.types import humanfriendly_parse_size_or_none, int_or_none, 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