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: The mean-field normal log posterior components (the quantity we want to approximate)
docstr_tok: ["The","mean-field","normal","log","posterior","components","(","the","quantity","we","want","to","approximate",")"]
code: def normal_log_q_initial(self, z):
      """
      The mean-field normal log posterior components (the quantity we want to approximate)
      """
      means, scale = self.get_means_and_scales_from_q()
      return (ss.norm.logpdf(z, loc=means, scale=scale)).sum(axis=1)
  
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 15 for calling the BBVI obj's underlying member method code with example usage: obj.normal_log_q_initial(z) and returns: ss