def get_topics_strings(topics_words, alpha, vocabulary,<EOL>topics_to_print=<NUM_LIT:10>, words_per_topic=<NUM_LIT:10>):
alpha = np.squeeze(alpha, axis=<NUM_LIT:0>)<EOL>highest_weight_topics = np.argsort(-alpha, kind="<STR_LIT>")<EOL>top_words = np.argsort(-topics_words, axis=<NUM_LIT:1>)<EOL>res = []<EOL>for topic_idx in highest_weight_topics[:topics_to_print]:<EOL><INDENT>l = ["<STR_LIT>".format(topic_idx, alpha[topic_idx])]<EOL>l += [vocabulary[word] for word in top_words[topic_idx, :words_per_topic]]<EOL>res.append("<STR_LIT:U+0020>".join(l))<EOL><DEDENT>return np.array(res)<EOL>
Returns the summary of the learned topics.

    Arguments:
      topics_words: KxV tensor with topics as rows and words as columns.
      alpha: 1xK tensor of prior Dirichlet concentrations for the
          topics.
      vocabulary: A mapping of word's integer index to the corresponding string.
      topics_to_print: The number of topics with highest prior weight to
          summarize.
      words_per_topic: Number of wodrs per topic to return.

    Returns:
      summary: A np.array with strings.