code: def train(args):
      utils.log_training_args(args, logger)
      model_file = build_model_filename(args)
  
      vocab_file = (
          args["save_dir"] + "/" + args["vocab_save_name"]
          if args["vocab_save_name"] is not None
          else "{}/{}_vocab.pt".format(
              args["save_dir"], args["shorthand"]
          )
      )
  
      if args["checkpoint"]:
          checkpoint_file = utils.checkpoint_name(
              args["save_dir"], model_file, args["checkpoint_save_name"]
          )
      else:
          checkpoint_file = None
  
      if os.path.exists(vocab_file):
          logger.info("Loading existing vocab file")
          vocab = load_char_vocab(vocab_file)
      else:
          logger.info("Building and saving vocab")
          vocab = {
              "char": build_charlm_vocab(
                  args["train_file"]
                  if args["train_dir"] is None
                  else args["train_dir"],
                  cutoff=args["cutoff"],
              )
          }
          torch.save(vocab["char"].state_dict(), vocab_file)
      logger.info(
          "Training model with vocab size: {}".format(
              len(vocab["char"])
          )
      )
  
      if checkpoint_file and os.path.exists(checkpoint_file):
          logger.info(
              "Loading existing checkpoint: %s" % checkpoint_file
          )
          trainer = CharacterLanguageModelTrainer.load(
              args, checkpoint_file, finetune=True
          )
      else:
          trainer = CharacterLanguageModelTrainer.from_new_model(
              args, vocab
          )
  
      writer = None
      if args["summary"]:
          from torch.utils.tensorboard import SummaryWriter
  
          summary_dir = (
              "{}/{}_summary".format(
                  args["save_dir"], args["save_name"]
              )
              if args["save_name"] is not None
              else "{}/{}_{}_charlm_summary".format(
                  args["save_dir"], args["shorthand"], args["direction"]
              )
          )
          writer = SummaryWriter(log_dir=summary_dir)
  
      # evaluate model within epoch if eval_interval is set
      eval_within_epoch = False
      if args["eval_steps"] > 0:
          eval_within_epoch = True
  
      if args["wandb"]:
          import wandb
  
          wandb_name = (
              args["wandb_name"]
              if args["wandb_name"]
              else "%s_%s_charlm"
              % (args["shorthand"], args["direction"])
          )
          wandb.init(name=wandb_name, config=args)
          wandb.run.define_metric("best_loss", summary="min")
          wandb.run.define_metric("ppl", summary="min")
  
      device = next(trainer.model.parameters()).device
  
      best_loss = None
      start_epoch = trainer.epoch  # will default to 1 for a new trainer
      for trainer.epoch in range(start_epoch, args["epochs"] + 1):
          # load train data from train_dir if not empty, otherwise load from file
          if args["train_dir"] is not None:
              train_path = args["train_dir"]
          else:
              train_path = args["train_file"]
          train_data = load_data(train_path, vocab, args["direction"])
          dev_data = load_file(
              args["eval_file"], vocab, args["direction"]
          )  # dev must be a single file
  
          # run over entire training set
          for data_chunk in train_data:
              batches = batchify(data_chunk, args["batch_size"], device)
              hidden = None
              total_loss = 0.0
              total_batches = math.ceil(
                  (batches.size(1) - 1) / args["bptt_size"]
              )
              iteration, i = 0, 0
              # over the data chunk
              while i < batches.size(1) - 1 - 1:
                  trainer.model.train()
                  trainer.global_step += 1
                  start_time = time.time()
                  bptt = (
                      args["bptt_size"]
                      if np.random.random() < 0.95
                      else args["bptt_size"] / 2.0
                  )
                  # prevent excessively small or negative sequence lengths
                  seq_len = max(5, int(np.random.normal(bptt, 5)))
                  # prevent very large sequence length, must be <= 1.2 x bptt
                  seq_len = min(seq_len, int(args["bptt_size"] * 1.2))
                  data, target = get_batch(batches, i, seq_len)
                  lens = [data.size(1) for i in range(data.size(0))]
  
                  trainer.optimizer.zero_grad()
                  output, hidden, decoded = trainer.model.forward(
                      data, lens, hidden
                  )
                  loss = trainer.criterion(
                      decoded.view(-1, len(vocab["char"])), target
                  )
                  total_loss += loss.data.item()
                  loss.backward()
  
                  torch.nn.utils.clip_grad_norm_(
                      trainer.params, args["max_grad_norm"]
                  )
                  trainer.optimizer.step()
  
                  hidden = repackage_hidden(hidden)
  
                  if (iteration + 1) % args["report_steps"] == 0:
                      cur_loss = total_loss / args["report_steps"]
                      elapsed = time.time() - start_time
                      logger.info(
                          "| epoch {:5d} | {:5d}/{:5d} batches | sec/batch {:.6f} | loss {:5.2f} | ppl {:8.2f}".format(
                              trainer.epoch,
                              iteration + 1,
                              total_batches,
                              elapsed / args["report_steps"],
                              cur_loss,
                              math.exp(cur_loss),
                          )
                      )
                      if args["wandb"]:
                          wandb.log(
                              {"train_loss": cur_loss},
                              step=trainer.global_step,
                          )
                      total_loss = 0.0
  
                  iteration += 1
                  i += seq_len
  
                  # evaluate if necessary
                  if (
                      eval_within_epoch
                      and trainer.global_step % args["eval_steps"] == 0
                  ):
                      _, ppl, best_loss = evaluate_and_save(
                          args,
                          vocab,
                          dev_data,
                          trainer,
                          best_loss,
                          model_file,
                          checkpoint_file,
                          writer,
                      )
                      if args["wandb"]:
                          wandb.log(
                              {
                                  "ppl": ppl,
                                  "best_loss": best_loss,
                                  "lr": get_current_lr(trainer, args),
                              },
                              step=trainer.global_step,
                          )
  
          # if eval_interval isn't provided, run evaluation after each epoch
          if not eval_within_epoch or trainer.epoch == args["epochs"]:
              _, ppl, best_loss = evaluate_and_save(
                  args,
                  vocab,
                  dev_data,
                  trainer,
                  best_loss,
                  model_file,
                  checkpoint_file,
                  writer,
              )
              if args["wandb"]:
                  wandb.log(
                      {
                          "ppl": ppl,
                          "best_loss": best_loss,
                          "lr": get_current_lr(trainer, args),
                      },
                      step=trainer.global_step,
                  )
  
      if writer:
          writer.close()
      if args["wandb"]:
          wandb.finish()
      return
  
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 14 for calling a global function with example usage: train(args) without return types