class_docstr: Black Box Variational Inference
  
  Parameters
  ----------
  neg_posterior : function
      posterior function
  
  q : List
      list holding distribution objects
  
  sims : int
      Number of Monte Carlo sims for the gradient
  
  step : float
      Step size for RMSProp
  
  iterations: int
      How many iterations to run
  
  record_elbo : boolean
      Whether to record the ELBO at every iteration
  
  quiet_progress : boolean
      Whether to print progress or stay quiet
docstr: Create logq components for mean-field normal family (the entropy estimate)
docstr_tok: ["Create","logq","components","for","mean-field","normal","family","(","the","entropy","estimate",")"]
code: def create_normal_logq(self, z):
      """
      Create logq components for mean-field normal family (the entropy estimate)
      """
      means, scale = self.get_means_and_scales()
      return ss.norm.logpdf(z, loc=means, scale=scale).sum()
  
imports: import sys
  import numpy
  import scipy.stats
  from .stoch_optim import RMSProp, ADAM
  from .bbvi_routines import alpha_recursion, log_p_posterior, mb_log_p_posterior
label_desc: Use image node_id 3 for calling the BBVI obj's underlying member method code with example usage: obj.create_normal_logq(z) and returns: ss