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: Draw parameters from a mean-field normal family
docstr_tok: ["Draw","parameters","from","a","mean-field","normal","family"]
code: def draw_normal_initial(self):
      """
      Draw parameters from a mean-field normal family
      """
      means, scale = self.get_means_and_scales_from_q()
      return np.random.normal(
          means, scale, size=[self.sims, means.shape[0]]
      ).T
  
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 8 for calling the BBVI obj's underlying member method code with example usage: obj.draw_normal_initial() and returns: np