code: def log_likelihood_bits(eval_data, predictions, scores, learner='ignored'):
      '''
      Return the log likelihood of each correct output in base 2 (bits),
      computed from the scores in `scores` (which should be in base e, nats).
  
      >>> bits = log_likelihood_bits(None, None, [np.log(0.5), np.log(0.125), np.log(0.25)])
      >>> [round(b) for b in bits]
      [-1.0, -3.0, -2.0]
      '''
      return (np.array(scores) / np.log(2.0)).tolist(f log_likelihood_bits(eval_data, predictions, scores, learner='ignored'):
      '''
      Return the log likelihood of each correct output in base 2 (bits),
      computed from the scores in `scores` (which should be in base e, nats).
  
      >>> bits = log_likelihood_bits(None, None, [np.log(0.5), np.log(0.125), np.log(0.25)])
      >>> [round(b) for b in bits]
      [-1.0, -3.0, -2.0]
      '''
      return (np.array(scores) / np.log(2.0)).tolist()
docs: Return the log likelihood of each correct output in base 2 (bits),
      computed from the scores in `scores` (which should be in base e, nats).
  
      >>> bits = log_likelihood_bits(None, None, [np.log(0.5), np.log(0.125), np.log(0.25)])
      >>> [round(b) for b in bits]
      [-1.0, -3.0, -2.0]