code: def soft_max(values):
      return [
          math.exp(val) / sum([math.exp(val) for val in values])
          for val in values
      ]
  
file_path: catboost/catboost/pytest/smoke_tests/classification_tutorial_cpu.py
imports: import os
  import pandas
  import os
  import numpy
  from sklearn.model_selection import train_test_split
  import sklearn
  from sklearn import metrics
  import matplotlib.pyplot
  from numpy import exp
  from copy import deepcopy
  import math
  import shap
  import shap
label_desc: Use image node_id 2 for calling a global function with example usage: soft_max(values) and returns: unknown