code: def get_parser():
      parser = argparse.ArgumentParser(
          description="Transformer for Neural Machine Translation."
      )
      parser.add_argument(
          "--train_src_corpus",
          type=str,
          help="The source training corpus.",
      )
      parser.add_argument(
          "--train_tgt_corpus",
          type=str,
          help="The target training corpus.",
      )
      parser.add_argument(
          "--dev_src_corpus", type=str, help="The source dev corpus."
      )
      parser.add_argument(
          "--dev_tgt_corpus",
          type=str,
          help="The target dev corpus after BPE tokenization.",
      )
      parser.add_argument(
          "--dev_tgt_raw_corpus",
          type=str,
          help="The target dev corpus before any tokenization (raw corpus).",
      )
      parser.add_argument(
          "--src_tokenizer",
          choices=[
              "spm",
              "subword_nmt",
              "yttm",
              "hf_bytebpe",
              "hf_wordpiece",
              "hf_bpe",
              "whitespace",
          ],
          default="whitespace",
          type=str,
          help="The source tokenizer. "
          "Whitespace tokenizer supports processing pre-encoded corpus, "
          "and the tokenizers besides whitespace supports online encoding.",
      )
      parser.add_argument(
          "--tgt_tokenizer",
          choices=[
              "spm",
              "subword_nmt",
              "yttm",
              "hf_bytebpe",
              "hf_wordpiece",
              "hf_bpe",
              "whitespace",
          ],
          default="whitespace",
          type=str,
          help="The target tokenizer.",
      )
      parser.add_argument(
          "--src_lang", required=True, help="The source language"
      )
      parser.add_argument(
          "--tgt_lang", required=True, help="The target language"
      )
      parser.add_argument(
          "--src_subword_model_path",
          type=str,
          help="Path to the source subword model.",
      )
      parser.add_argument(
          "--src_vocab_path", type=str, help="Path to the source vocab."
      )
      parser.add_argument(
          "--tgt_subword_model_path",
          type=str,
          help="Path to the target subword model.",
      )
      parser.add_argument(
          "--tgt_vocab_path", type=str, help="Path to the target vocab."
      )
      parser.add_argument(
          "--seed", type=int, default=100, help="The random seed."
      )
      parser.add_argument(
          "--optimizer", type=str, default="adam", help="The optimizer."
      )
      parser.add_argument(
          "--optimizer_params",
          type=str,
          default='{"beta1": 0.9, "beta2": 0.997, "epsilon": 1e-09}',
          help="The optimizer parameters.",
      )
      parser.add_argument(
          "--epochs",
          type=int,
          default=30,
          help="Upper epoch limit, "
          "the model will keep training when epochs < 0 and max_update < 0.",
      )
      parser.add_argument(
          "--max_update",
          type=int,
          default=-1,
          help="Max update steps, when max_update > 0, epochs will be set to -1, "
          "each update step contains gpu_num * num_accumulated batches.",
      )
      parser.add_argument(
          "--save_interval_update",
          type=int,
          default=500,
          help="Update interval of saving checkpoints while using max_update.",
      )
      parser.add_argument(
          "--cfg",
          type=str,
          default="transformer_base",
          help="Configuration of the transformer model. "
          "You may select a yml file or use the prebuild configurations.",
      )
      parser.add_argument(
          "--label_smooth_alpha",
          type=float,
          default=0.1,
          help="Weight of label smoothing",
      )
      parser.add_argument(
          "--wd", type=float, default=0.0, help="Weight decay"
      )
      parser.add_argument(
          "--sampler",
          type=str,
          choices=["BoundedBudgetSampler", "FixedBucketSampler"],
          default="FixedBucketSampler",
          help="Type of sampler",
      )
      parser.add_argument(
          "--max_src_length",
          type=int,
          default=None,
          help="Maximum source length. We will trim the tokens in the source "
          "sentence if it is longer than this number.",
      )
      parser.add_argument(
          "--max_tgt_length",
          type=int,
          default=None,
          help="Maximum target length. We will trim the tokens in the target "
          "sentence if it is longer than this number.",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=2700,
          help="Batch size. Number of tokens per gpu in a minibatch.",
      )
      parser.add_argument(
          "--val_batch_size",
          type=int,
          default=4,
          help="Batch size for evaluation.",
      )
      parser.add_argument(
          "--max_grad_norm",
          type=float,
          default=None,
          help="Max gradient norm. when not specified, the gradient "
          "clipping will not be used.",
      )
      parser.add_argument(
          "--num_buckets", type=int, default=20, help="Bucket number."
      )
      parser.add_argument(
          "--bucket_scheme",
          type=str,
          default="exp",
          help="Strategy for generating bucket keys. It supports: "
          '"constant": all the buckets have the same width; '
          '"linear": the width of bucket increases linearly; '
          '"exp": the width of bucket increases exponentially',
      )
      parser.add_argument(
          "--bucket_ratio",
          type=float,
          default=0.0,
          help="Ratio for increasing the throughput of the bucketing",
      )
      parser.add_argument(
          "--max_num_tokens",
          type=int,
          default=-1,
          help="max tokens num of each batch, applicable while using BoundedBudgetSampler",
      )
      parser.add_argument(
          "--max_num_sentences",
          type=int,
          default=-1,
          help="max sentences num of each batch, applicable while using BoundedBudgetSampler",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.002,
          help="The learning rate at the end of the warmup stage. "
          "If it is not given, we will use the formula suggested in the "
          "original Transformer paper:"
          " 1.0 / sqrt(d_model) / sqrt(warmup_steps). "
          "Otherwise, we will use the given lr as the final learning rate in "
          "the warmup phase.",
      )
      parser.add_argument(
          "--beam-size", type=int, default=4, help="Number of beams"
      )
      parser.add_argument(
          "--lp_alpha",
          type=float,
          default=1.0,
          help="The alpha value in the length penalty term",
      )
      parser.add_argument(
          "--lp_k",
          type=int,
          default=5,
          help="The K value in the length penalty term.",
      )
      parser.add_argument(
          "--max_length_a",
          type=int,
          default=1,
          help="The a in the a * x + b formula of beam search",
      )
      parser.add_argument(
          "--max_length_b",
          type=int,
          default=50,
          help="The b in the a * x + b formula of beam search",
      )
      parser.add_argument(
          "--warmup_steps",
          type=int,
          default=4000,
          help="number of warmup steps used in NOAM's stepsize schedule",
      )
      parser.add_argument(
          "--warmup_init_lr",
          type=float,
          default=0.0,
          help="Initial learning rate at the beginning of the warm-up stage",
      )
      parser.add_argument(
          "--num_accumulated",
          type=int,
          default=32,
          help="Number of steps to accumulate the gradients. "
          "This is useful to mimic large batch training with limited gpu memory",
      )
      parser.add_argument(
          "--magnitude",
          type=float,
          default=3.0,
          help="Magnitude of Xavier initialization",
      )
      parser.add_argument(
          "--num_averages",
          type=int,
          default=-1,
          help="Perform final testing based on the "
          "average of last num_averages checkpoints. "
          "Use num_average will cause extra gpu memory usage.",
      )
      parser.add_argument(
          "--log_interval",
          type=int,
          default=10,
          metavar="N",
          help="report interval",
      )
      parser.add_argument(
          "--save_dir",
          type=str,
          default="transformer_out",
          help="directory path to save the final model and training log",
      )
      parser.add_argument("--overwrite_cache", action="store_true")
      parser.add_argument(
          "--fp16",
          action="store_true",
          help="Whether to use dtype float16",
      )
      parser.add_argument(
          "--comm_backend",
          type=str,
          default="device",
          choices=["horovod", "dist_sync_device", "device"],
          help="Communication backend.",
      )
      parser.add_argument(
          "--tokenize",
          action="store_true",
          help="Whether to tokenize the input, By default, we assume all input has "
          "been pretokenized. "
          "When set the flag, we will tokenize the samples.",
      )
      parser.add_argument(
          "--gpus",
          type=str,
          help="list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu.",
      )
      return parser
  
imports: import argparse
  import time
  import random
  import os
  import logging
  import json
  import math
  import numpy
  import mxnet
  import sacrebleu
  from mxnet import gluon
  from gluonnlp.models.transformer import TransformerModel, TransformerInference
  from gluonnlp.sequence_sampler import BeamSearchSampler, BeamSearchScorer
  from gluonnlp.utils.misc import logging_config, md5sum, grouper, init_comm, repeat
  from gluonnlp.data.sampler import ConstWidthBucket, LinearWidthBucket, ExpWidthBucket, FixedBucketSampler, BoundedBudgetSampler, ShardedIterator
  from tensorboardX import SummaryWriter
  import gluonnlp.data.batchify
  from gluonnlp.data import Vocab
  from gluonnlp.data import tokenizers
  from gluonnlp.data.tokenizers import BaseTokenizerWithVocab, huggingface, MosesTokenizer
  from gluonnlp.lr_scheduler import InverseSquareRootScheduler
  from gluonnlp.loss import LabelSmoothCrossEntropyLoss
  from gluonnlp.utils.parameter import grad_global_norm, clip_grad_global_norm, deduplicate_param_dict, count_parameters, AverageSGDTracker
label_desc: Use image node_id 1 for calling a global function with example usage: get_parser() and returns: parser