code: def create_tokenizer(tokenizer_type, model_path, vocab_path):
      if tokenizer_type == "spm":
          return tokenizers.create(
              tokenizer_type, model_path=model_path, vocab=vocab_path
          )
      elif tokenizer_type == "subword_nmt":
          return tokenizers.create(
              tokenizer_type, model_path=model_path, vocab=vocab_path
          )
      elif tokenizer_type == "yttm":
          return tokenizers.create(
              tokenizer_type, model_path=model_path, vocab=vocab_path
          )
      elif tokenizer_type in ["hf_bytebpe", "hf_wordpiece", "hf_bpe"]:
          if huggingface.is_new_version_model_file(model_path):
              return tokenizers.create(
                  "hf_tokenizer",
                  model_path=model_path,
                  vocab=vocab_path,
              )
          elif tokenizer_type == "hf_bytebpe":
              return tokenizers.create(
                  tokenizer_type,
                  merges_file=model_path,
                  vocab_file=vocab_path,
              )
          elif tokenizer_type == "hf_wordpiece":
              return tokenizers.create(
                  tokenizer_type, vocab_file=vocab_path
              )
          elif tokenizer_type == "hf_bpe":
              return tokenizers.create(
                  tokenizer_type,
                  merges_file=model_path,
                  vocab_file=vocab_path,
              )
      else:
          raise NotImplementedError
  
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 3 for calling a global function with example usage: create_tokenizer(tokenizer_type, model_path, vocab_path) and returns: tokenizers, tokenizers, tokenizers, tokenizers, tokenizers, tokenizers, tokenizers