code: def create_discrete_column(num_samples, std_dev=1):
      # Generating a random normal distribution of integers
      x = np.arange(-5, 6)
      xU, xL = x + 0.5, x - 0.5
      prob = ss.norm.cdf(xU, scale=std_dev) - ss.norm.cdf(
          xL, scale=std_dev
      )  # probability of selecting a number x is p(x-0.5 < x < x+0.5) where x is a normal random variable with mean 0 and standard deviation std_dev
      prob = (
          prob / prob.sum()
      )  # normalize the probabilities so their sum is 1
      nums = choice(a=x, size=num_samples, p=prob)  # pick up an element
      return nums
  
imports: import math
  import string
  import networkx
  import numpy
  import pandas
  import scipy.stats
  from numpy.random import choice
  from scipy.stats import bernoulli, halfnorm, poisson, uniform
  from sklearn.neural_network import MLPRegressor
  from dowhy.utils.graph_operations import add_edge, del_edge, get_random_node_pair, get_simple_ordered_tree
label_desc: Use image node_id 11 for calling a global function with example usage: create_discrete_column(num_samples, std_dev) and returns: nums