code: def _softmax(z, p, xb):
      """
      Softmax function. This function is used to reversely solve the constant root value in the linear part to make the softmax function output mean to be a given value.
  
      Parameters
      ----------
      z : float
          Constant value in the linear part.
      p : float
          The target output mean value.
      xb : list
          An array, with each element as the sum of product of coefficient and feature value
      """
      sm_arr = expit(z + np.array(xb))
      res = p - np.mean(sm_arr)
      return res
  
imports: import random
  import numpy
  import pandas
  from sklearn.datasets import make_classification
  from scipy.optimize import fsolve
  from scipy.special import expit
  from scipy.special import logit
label_desc: Use image node_id 11 for calling a global function with example usage: _softmax(z, p, xb) and returns: res