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: Gets the mean and scales for normal approximating parameters
docstr_tok: ["Gets","the","mean","and","scales","for","normal","approximating","parameters"]
code: def get_means_and_scales_from_q(self):
      """
      Gets the mean and scales for normal approximating parameters
      """
      means = np.zeros(len(self.q))
      scale = np.zeros(len(self.q))
      for i in range(len(self.q)):
          means[i] = self.q[i].mu0
          scale[i] = self.q[i].sigma0
      return means, scale
  
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 10 for calling the BBVI obj's underlying member method code with example usage: obj.get_means_and_scales_from_q() and returns: means,  scale