code: def initializer(self):
      # Use Encoder class as a container for global data
      Encoder.tokenizer = build_tokenizer(self.args)
      if self.args.split_sentences:
          if not nltk_available:
              print("NLTK is not available to split sentences.")
              exit()
          if os.environ.get("NLTK_DATA"):
              library = os.path.join(
                  os.environ.get("NLTK_DATA"),
                  "tokenizers",
                  "punkt",
                  f"{self.args.lang}.pickle",
              )
              url = f"file:{library}"
          else:
              library = os.path.join(
                  "tokenizers", "punkt", f"{self.args.lang}.pickle"
              )
              url = f"nltk:{library}"
          splitter = nltk.load(url)
          if self.args.keep_newlines:
              # this prevents punkt from eating newlines after sentences
              Encoder.splitter = (
                  nltk.tokenize.punkt.PunktSentenceTokenizer(
                      train_text=splitter._params,
                      lang_vars=CustomLanguageVars(),
                  )
              )
          else:
              Encoder.splitter = splitter
  
      else:
          Encoder.splitter = IdentitySplitter()
  
imports: import argparse
  import math
  import json
  import os
  import sys
  import time
  import gzip
  import glob
  import torch
  import numpy
  import multiprocessing
  from megatron.tokenizer import build_tokenizer
  from megatron.core.datasets import indexed_dataset
label_desc: Use image node_id 2 for calling the Encoder obj's underlying member method code with example usage: obj.initializer() without return types