code: def answer_question(
      self, question, context, max_chunk_size=512, stride=70
  ):
      # chunk tokens
      chunked_tokens = self.token_chunker(
          question, context, max_chunk_size, stride
      )
      answer_holder = []
      for chunk in chunked_tokens:
          model_input = {
              "input_ids": chunk["token_ids"],
              "attention_mask": chunk["attention_mask"],
              "token_type_ids": chunk["token_type_ids"],
          }
          answer = self.get_chunk_answer_span(model_input)
          if len(answer["answer"]) > 2:
              answer["question"] = question
              answer["context"] = (
                  chunk["context"]
                  .replace("##", "")
                  .replace(
                      answer["answer"],
                      " <em>" + answer["answer"] + "</em> ",
                  )
              )
              answer_holder.append(answer)
      return answer_holder
  
file_path: neuralqa/neuralqa/reader/bertreader.py
imports: from neuralqa.reader import Reader
  import tensorflow
  import numpy
  import time
  import logging
label_desc: Use image node_id 5 for calling the BERTReader obj's underlying member method code with example usage: obj.answer_question(question, context, max_chunk_size, stride) and returns: answer_holder