code: def answer_query(query, index, n_results=10):
      documents, vocabulary, tf, idf, pagerank = index
      # document_norm = sp.linalg.norm(tf, axis=1)
  
      query_tokens = nltk.word_tokenize(query.lower())
      indices = [vocabulary[t] for t in query_tokens if t in vocabulary]
  
      if not indices:
          print(
              "\nCannot find documents relevant to the specified query"
          )
          return
  
      document_vector = tf[:, indices]
      query_vector = idf[indices]
      rank = document_vector.dot(query_vector)  # / document_norm
  
      (document_ids,) = rank.nonzero()
  
      similarity_score = rank[document_ids].T
      pagerank_score = pagerank[document_ids]
      rank = similarity_score + pagerank_score
      order = np.asarray(rank.argsort())
  
      print("Found {} relevant documents".format(len(document_ids)))
  
      order = order[:n_results]
      for i, index in enumerate(reversed(order), start=1):
          document_id = document_ids[index]
          document = documents[document_id]
          score = rank[index]
  
          print("")
          print("{}) {}".format(i, document["uri"]))
          print("   Total Score: {}".format(score))
          print("   PageRank Score: {}".format(pagerank_score[index]))
          print(
              "   Similarity Score: {}".format(similarity_score[index])
          )
  
file_path: neupy/site/search-index/search.py
imports: import os
  import pickle
  import argparse
  import nltk
  import numpy
  import scipy.sparse
  from build import INDEX_FILE
label_desc: Use image node_id 2 for calling a global function with example usage: answer_query(query, index, n_results) without return types