code: def find_answer_sentence(answer_pos: int, context: str) -> str:
      answer_sentence = ""
      context_sentences = nltk.sent_tokenize(context)
      start = 0
      context_sentences_offsets = []
      for sentence in context_sentences:
          end = start + len(sentence)
          context_sentences_offsets.append((start, end))
          start = end + 1
  
      for sentence, (start_offset, end_offset) in zip(
          context_sentences, context_sentences_offsets
      ):
          if start_offset < answer_pos < end_offset:
              answer_sentence = sentence
              break
  
      return answer_sentence
  
imports: from typing import Any, List
  import nltk
  from deeppavlov.core.common.registry import register
label_desc: Use image node_id 2 for calling a global function with example usage: find_answer_sentence(answer_pos, context) and returns: answer_sentence