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 the approximating distributions
docstr_tok: ["Draw","parameters","from","the","approximating","distributions"]
code: def draw_variables(self):
      """
      Draw parameters from the approximating distributions
      """
      z = self.q[0].draw_variable_local(self.sims)
      for i in range(1, len(self.q)):
          z = np.vstack((z, self.q[i].draw_variable_local(self.sims)))
      return z
  
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 9 for calling the BBVI obj's underlying member method code with example usage: obj.draw_variables() and returns: z