code: def predict(self, X, top_k_list):
      assert X.shape[0] == len(top_k_list)
      probs = numpy.asarray(super(TopKRanker, self).predict_proba(X))
      all_labels = []
      for i, k in enumerate(top_k_list):
          probs_ = probs[i, :]
          labels = self.classes_[probs_.argsort()[-k:]].tolist()
          all_labels.append(labels)
      return all_labels
  
file_path: deepwalk/example_graphs/scoring.py
imports: import numpy
  import sys
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  from collections import defaultdict
  from gensim.models import Word2Vec, KeyedVectors
  from six import iteritems
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import f1_score
  from scipy.io import loadmat
  from sklearn.utils import shuffle
  from sklearn.preprocessing import MultiLabelBinarizer
label_desc: Use image node_id 1 for calling the TopKRanker obj's underlying member method code with example usage: obj.predict(X, top_k_list) and returns: all_labels