code: def normal_cdf(x, mu, sigma):
      top = x - mu
      bottom = np.maximum(np.sqrt(2) * sigma, EPS)
      z = top / bottom
      return 0.5 * (1 + erf(z))
  
imports: import logging
  import time
  import numpy
  from scipy.special import erf
  from .None import pyll
  from .pyll import scope
  from .pyll.stochastic import implicit_stochastic
  from .base import miscs_to_idxs_vals
  from .base import miscs_update_idxs_vals
  from .None import rand
label_desc: Use image node_id 5 for calling a global function with example usage: normal_cdf(x, mu, sigma) and returns: unknown