code: def get_softmax(scores, threshold=None):
      exp_scores = np.exp(scores)
  
      for _ in exp_scores:
          for sentence in exp_scores:
              for i in range(len(sentence)):
                  probabilities = sentence[i] / np.sum(sentence[i])
                  sentence[i] = [
                      p if threshold is None or p >= threshold else 0
                      for p in probabilities
                  ]
  
      return exp_scores
  
imports: import math
  import random
  import sys
  import numpy
  import tensorflow
  from .sentence_grouper import SentenceGrouper
  from ..tf2contrib import *
label_desc: Use image node_id 23 for calling the NerModel obj's underlying member method code with example usage: obj.get_softmax(scores, threshold) and returns: exp_scores