docstr: This interface returns the k keywords under specific topic.
  
  Args:
      topic_id(int): topic information we want to know.
      k(int): top k keywords.
  
  Returns:
      results(dict): contains specific topic's keywords and corresponding
                     probability.
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code: def show_topic_keywords(self, topic_id, k=10):
      """
      This interface returns the k keywords under specific topic.
  
      Args:
          topic_id(int): topic information we want to know.
          k(int): top k keywords.
  
      Returns:
          results(dict): contains specific topic's keywords and corresponding
                         probability.
      """
      EPS = 1e-8
      results = {}
      if 0 <= topic_id < self.config.num_topics:
          k = min(k, len(self.topic_words[topic_id]))
          for i in range(k):
              prob = self.topic_words[topic_id][i].count / (
                  self.topic_sum_table[topic_id] + EPS
              )
              results[
                  self.vocabulary[self.topic_words[topic_id][i].word_id]
              ] = prob
          return results
      else:
          logger.error("%d is out of range!" % topic_id)
  
file_path: PaddleHub/modules/text/language_model/slda_novel/module.py
imports: import os
  import paddlehub
  from paddlehub.module.module import moduleinfo
  from paddlehub.common.logger import logger
  from slda_novel.inference_engine import InferenceEngine
  from slda_novel.document import SLDADoc
  from slda_novel.semantic_matching import SemanticMatching, WordAndDis
  from slda_novel.tokenizer import LACTokenizer, SimpleTokenizer
  from slda_novel.config import ModelType
  from slda_novel.vocab import Vocab, WordCount
label_desc: Use image node_id 3 for calling the TopicModel obj's underlying member method code with example usage: obj.show_topic_keywords(topic_id, k) and returns: results