code: def encode(self, json_line):
      if not self.args.text_file:
          data = json.loads(json_line)
          ids = {}
          for key in self.args.json_keys:
              text = data[key]
              if self.args.apply_ftfy:
                  text = ftfy.fix_text(text)
              doc_ids = []
              for sentence in Encoder.splitter.tokenize(text):
                  sentence_ids = Encoder.tokenizer.text_to_ids(sentence)
                  if len(sentence_ids) > 0:
                      doc_ids.append(sentence_ids)
              if len(doc_ids) > 0 and self.args.append_eod:
                  doc_ids[-1].append(Encoder.tokenizer.eos_id)
              ids[key] = doc_ids
      else:
          data = json_line
          ids = {}
          text = data.strip()
          if self.args.apply_ftfy:
              text = ftfy.fix_text(text)
          doc_ids = []
          for sentence in Encoder.splitter.tokenize(text):
              sentence_ids = Encoder.tokenizer.text_to_ids(sentence)
              if len(sentence_ids) > 0:
                  doc_ids.append(sentence_ids)
          if len(doc_ids) > 0 and self.args.append_eod:
              doc_ids[-1].append(Encoder.tokenizer.eos_id)
          ids["text"] = doc_ids
      return ids, len(json_line)
  
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 3 for calling the Encoder obj's underlying member method code with example usage: obj.encode(json_line) and returns: ids,  len