code: def initializer(self):
      # Use Encoder class as a container for global data
      Encoder.tokenizer = get_tokenizer(self.args)
  
      if self.args.split_sentences:
          if not nltk_available:
              print("NLTK is not available to split sentences.")
              exit()
          splitter = nltk.load("tokenizers/punkt/english.pickle")
          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 gzip
  import json
  import multiprocessing
  import os
  import pathlib
  import sys
  import time
  import ftfy
  import torch
  from nemo.collections.nlp.data.language_modeling.megatron import indexed_dataset
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
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