code: def spm_wnorm(A):
      """
      Returns Expectation of logarithm of Dirichlet parameters over a set of
      Categorical distributions, stored in the columns of A.
      """
      A = A + EPS_VAL
      norm = np.divide(1.0, np.sum(A, axis=0))
      avg = np.divide(1.0, A)
      wA = norm - avg
      return wA
  
imports: import numpy
  from scipy import special
  from pymdp import utils
  from itertools import chain
label_desc: Use image node_id 11 for calling a global function with example usage: spm_wnorm(A) and returns: wA