code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Transformer for Neural Machine Translation. Load a checkpoint and inference."
      )
      parser.add_argument(
          "--seed", type=int, default=100, help="The random seed."
      )
      parser.add_argument(
          "--src_lang", type=str, default="en", help="Source language"
      )
      parser.add_argument(
          "--tgt_lang", type=str, default="de", help="Target language"
      )
      parser.add_argument(
          "--src_corpus",
          type=str,
          required=True,
          help="The source corpus for evaluation.",
      )
      parser.add_argument(
          "--tgt_corpus",
          type=str,
          default=None,
          help="The target corpus for evaluation.",
      )
      parser.add_argument(
          "--src_normalizer",
          choices=["no", "moses"],
          default="moses",
          help="The sentence normalizer that will be "
          "used to normalize the source sentence.",
      )
      parser.add_argument(
          "--src_base_tokenizer",
          choices=["whitespace", "moses", "no"],
          default="moses",
          help="The base tokenizer to tokenize the target "
          "sentence into a list of tokens.",
      )
      parser.add_argument(
          "--src_tokenizer",
          choices=[
              "spm",
              "subword_nmt",
              "yttm",
              "hf_bytebpe",
              "hf_wordpiece",
              "hf_bpe",
          ],
          required=True,
          type=str,
          help="The source subword tokenizer. "
          "Only supports online encoding at present.",
      )
      parser.add_argument(
          "--tgt_normalizer",
          choices=["no", "moses"],
          default="moses",
          help="The sentence normalizer that will be "
          "used to normalize the target sentence.",
      )
      parser.add_argument(
          "--tgt_base_tokenizer",
          choices=["whitespace", "moses", "no"],
          default="moses",
          help="The base tokenizer to tokenize the source "
          "sentence into a list of tokens.",
      )
      parser.add_argument(
          "--tgt_tokenizer",
          choices=[
              "spm",
              "subword_nmt",
              "yttm",
              "hf_bytebpe",
              "hf_wordpiece",
              "hf_bpe",
          ],
          required=True,
          type=str,
          help="The target tokenizer. Only supports online encoding at present.",
      )
      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 subword 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 subword vocab.",
      )
      parser.add_argument(
          "--src_max_len",
          type=int,
          default=None,
          help="Maximum length of the source sentence.",
      )
      parser.add_argument(
          "--tgt_max_len",
          type=int,
          default=None,
          help="Maximum length of the target sentence.",
      )
      parser.add_argument(
          "--cfg",
          type=str,
          help="Config file of the Transformer model.",
      )
      parser.add_argument(
          "--beam-size", type=int, default=4, help="Number of beams"
      )
      parser.add_argument(
          "--lp_alpha",
          type=float,
          default=0.6,
          help="The alpha value in the length penalty",
      )
      parser.add_argument(
          "--lp_k",
          type=int,
          default=5,
          help="The K value in the length penalty",
      )
      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(
          "--param_path",
          type=str,
          help="The path to the model parameters.",
      )
      parser.add_argument(
          "--gpus",
          type=str,
          default="0",
          help="List of gpus to run, e.g. 0 or 0,2,5. empty means using cpu."
          "(using single gpu is suggested)",
      )
      parser.add_argument(
          "--save_dir",
          type=str,
          default=None,
          help="The path to save the log files and predictions.",
      )
      parser.add_argument(
          "--stochastic",
          action="store_true",
          help="Whether to use the stochastic beam search",
      )
      parser.add_argument(
          "--temperature",
          type=float,
          default=None,
          help="the temperature used for softmax normalization with stochastic setting",
      )
      parser.add_argument(
          "--inference",
          action="store_true",
          help="Whether to inference with your own data, "
          "when applying inference, tgt_corpus is not needed and will be set to None.",
      )
      parser.add_argument(
          "--fp16",
          action="store_true",
          help="Whether to use dtype float16",
      )
      args = parser.parse_args()
      if args.save_dir is None:
          args.save_dir = (
              os.path.splitext(args.param_path)[0] + "_evaluation"
          )
      assert (
          args.inference or args.tgt_corpus
      ), "requring --tgt_corpus while not using --inference"
      if args.inference:
          args.tgt_corpus = None
      if args.stochastic:
          if args.temperature is None:
              args.temperature = 0.5
      logging_config(args.save_dir, console=True)
      logging.info(args)
      return args
  
imports: import numpy
  import random
  import os
  import json
  import mxnet
  from mxnet import gluon
  import argparse
  import logging
  import time
  from gluonnlp.utils.misc import logging_config
  from gluonnlp.models.transformer import TransformerModel, TransformerInference
  from gluonnlp.data.batchify import Tuple, Pad, Stack
  from gluonnlp.data.filtering import MosesNormalizer
  from gluonnlp.data import tokenizers
  from gluonnlp.data.tokenizers import huggingface
  from gluonnlp.sequence_sampler import BeamSearchSampler, BeamSearchScorer
  import sacrebleu
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args