code: def _improve_answer_span(
      doc_tokens: List[str],
      input_start: int,
      input_end: int,
      tokenizer: object,
      orig_answer_text: str,
  ):
      """Returns tokenized answer spans that better match the annotated answer."""
      tok_answer_text = " ".join(
          tokenizer.text_to_tokens(orig_answer_text)
      )
  
      for new_start in range(input_start, input_end + 1):
          for new_end in range(input_end, new_start - 1, -1):
              text_span = " ".join(
                  doc_tokens[new_start : (new_end + 1)]
              )
              if text_span == tok_answer_text:
                  return (new_start, new_end)
  
      return (input_start, input_end)
  
imports: import collections
  from typing import List
  import ijson
  import numpy
  from transformers.models.bert.tokenization_bert import BasicTokenizer
  from nemo.collections.nlp.data.data_utils import DataProcessor, check_chinese_char, normalize_answer, normalize_chinese_answer
  from nemo.utils import logging
label_desc: Use image node_id 11 for calling a global function with example usage: _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text) and returns: input_start,  input_end, new_start,  new_end