code: def top_k_triplets(predictions, labels, k=20):
      """Get the top_k for a 1-d numpy array. Returns a sparse list of tuples in
      (prediction, class) format"""
      m = len(predictions)
      k = min(k, m)
      indices = numpy.argpartition(predictions, -k)[-k:]
      return [
          (index, predictions[index], labels[index])
          for index in indices
      ]
  
file_path: PaddleHub/modules/video/classification/videotag_tsn_lstm/resource/metrics/youtube8m/eval_util.py
imports: import datetime
  import numpy
  from .None import mean_average_precision_calculator
  from .None import average_precision_calculator
label_desc: Use image node_id 6 for calling a global function with example usage: top_k_triplets(predictions, labels, k) and returns: unknown