code: def build_argparse():
      parser = argparse.ArgumentParser(
          formatter_class=argparse.ArgumentDefaultsHelpFormatter
      )
      parser.add_argument(
          "--train_file", type=str, help="Input plaintext file"
      )
      parser.add_argument(
          "--train_dir",
          type=str,
          help="If non-empty, load from directory with multiple training files",
      )
      parser.add_argument(
          "--eval_file",
          type=str,
          help="Input plaintext file for the dev/test set",
      )
      parser.add_argument(
          "--shorthand", type=str, help="UD treebank shorthand"
      )
  
      parser.add_argument(
          "--mode", default="train", choices=["train", "predict"]
      )
      parser.add_argument(
          "--direction",
          default="forward",
          choices=["forward", "backward"],
          help="Forward or backward language model",
      )
      parser.add_argument(
          "--forward",
          action="store_const",
          dest="direction",
          const="forward",
          help="Train a forward language model",
      )
      parser.add_argument(
          "--backward",
          action="store_const",
          dest="direction",
          const="backward",
          help="Train a backward language model",
      )
  
      parser.add_argument(
          "--char_emb_dim",
          type=int,
          default=100,
          help="Dimension of unit embeddings",
      )
      parser.add_argument(
          "--char_hidden_dim",
          type=int,
          default=1024,
          help="Dimension of hidden units",
      )
      parser.add_argument(
          "--char_num_layers",
          type=int,
          default=1,
          help="Layers of RNN in the language model",
      )
      parser.add_argument(
          "--char_dropout",
          type=float,
          default=0.05,
          help="Dropout probability",
      )
      parser.add_argument(
          "--char_unit_dropout",
          type=float,
          default=1e-5,
          help="Randomly set an input char to UNK during training",
      )
      parser.add_argument(
          "--char_rec_dropout",
          type=float,
          default=0.0,
          help="Recurrent dropout probability",
      )
  
      parser.add_argument(
          "--batch_size",
          type=int,
          default=100,
          help="Batch size to use",
      )
      parser.add_argument(
          "--bptt_size",
          type=int,
          default=250,
          help="Sequence length to consider at a time",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=50,
          help="Total epochs to train the model for",
      )
      parser.add_argument(
          "--max_grad_norm",
          type=float,
          default=0.25,
          help="Maximum gradient norm to clip to",
      )
      parser.add_argument(
          "--lr0", type=float, default=5, help="Initial learning rate"
      )
      parser.add_argument(
          "--anneal",
          type=float,
          default=0.25,
          help="Anneal the learning rate by this amount when dev performance deteriorate",
      )
      parser.add_argument(
          "--patience",
          type=int,
          default=1,
          help="Patience for annealing the learning rate",
      )
      parser.add_argument(
          "--weight_decay", type=float, default=0.0, help="Weight decay"
      )
      parser.add_argument(
          "--momentum",
          type=float,
          default=0.0,
          help="Momentum for SGD.",
      )
      parser.add_argument(
          "--cutoff",
          type=int,
          default=1000,
          help="Frequency cutoff for char vocab. By default we assume a very large corpus.",
      )
  
      parser.add_argument(
          "--report_steps",
          type=int,
          default=50,
          help="Update step interval to report loss",
      )
      parser.add_argument(
          "--eval_steps",
          type=int,
          default=100000,
          help="Update step interval to run eval on dev; set to -1 to eval after each epoch",
      )
      parser.add_argument(
          "--save_name",
          type=str,
          default=None,
          help="File name to save the model",
      )
      parser.add_argument(
          "--vocab_save_name",
          type=str,
          default=None,
          help="File name to save the vocab",
      )
      parser.add_argument(
          "--checkpoint_save_name",
          type=str,
          default=None,
          help="File name to save the most recent checkpoint",
      )
      parser.add_argument(
          "--no_checkpoint",
          dest="checkpoint",
          action="store_false",
          help="Don't save checkpoints",
      )
      parser.add_argument(
          "--save_dir",
          type=str,
          default="saved_models/charlm",
          help="Directory to save models in",
      )
      parser.add_argument(
          "--summary",
          action="store_true",
          help="Use summary writer to record progress.",
      )
      utils.add_device_args(parser)
      parser.add_argument("--seed", type=int, default=1234)
  
      parser.add_argument(
          "--wandb",
          action="store_true",
          help="Start a wandb session and write the results of training.  Only applies to training.  Use --wandb_name instead to specify a name",
      )
      parser.add_argument(
          "--wandb_name",
          default=None,
          help="Name of a wandb session to start when training.  Will default to the dataset short name",
      )
      return parser
  
imports: import argparse
  from copy import copy
  import logging
  import lzma
  import math
  import os
  import random
  import time
  from types import GeneratorType
  import numpy
  import torch
  from stanza.models.common.char_model import build_charlm_vocab, CharacterLanguageModel, CharacterLanguageModelTrainer
  from stanza.models.common.vocab import CharVocab
  from stanza.models.common import utils
  from stanza.models import _training_logging
label_desc: Use image node_id 6 for calling a global function with example usage: build_argparse() and returns: parser