code: def _find_relevance(log_ttd, log_lift, R, lambda_):
      relevance = lambda_ * log_ttd + (1 - lambda_) * log_lift
      return relevance.T.apply(lambda topic: topic.nlargest(R).index)
  
imports: import json
  import logging
  import numpy
  import pandas
  from collections import namedtuple
  from joblib import Parallel, delayed, cpu_count
  from scipy.stats import entropy
  from scipy.spatial.distance import pdist, squareform
  from sklearn.manifold import MDS, TSNE
  from pyLDAvis.utils import NumPyEncoder
label_desc: Use image node_id 14 for calling a global function with example usage: _find_relevance(log_ttd, log_lift, R, lambda_) and returns: relevance