code: def apply_get_from_cache(
      self,
      sentence: str,
      context: bool = False,
      indexs: List[int] = None,
  ):
      if context and indexs is not None:
          if sentence in self.cache:
              elmo_full_vec = self.cache[sentence]
          else:
              elmo_full_vec = self.embeder.get_vector(sentence.split())
              self.cache[sentence] = elmo_full_vec
  
          elmo_ret_vec = self.get_mention_vec_from_sent(
              elmo_full_vec, indexs
          )
      else:
          if sentence in self.cache:
              elmo_ret_vec = self.cache[sentence]
          else:
              elmo_ret_vec = self.get_elmo_avg(sentence.split())
              self.cache[sentence] = elmo_ret_vec
  
      return elmo_ret_vec
  
file_path: nlp-architect/nlp_architect/data/cdc_resources/embedding/embed_elmo.py
imports: import logging
  import pickle
  from typing import List
  import numpy
  from nlp_architect.common.cdc.mention_data import MentionDataLight
  from nlp_architect.utils.embedding import ELMoEmbedderTFHUB
label_desc: Use image node_id 3 for calling the ElmoEmbedding obj's underlying member method code with example usage: obj.apply_get_from_cache(sentence, context, indexs) and returns: elmo_ret_vec