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: Obtains an array with the current parameters
docstr_tok: ["Obtains","an","array","with","the","current","parameters"]
code: def current_parameters(self):
      """
      Obtains an array with the current parameters
      """
      current = []
      for core_param in range(len(self.q)):
          for approx_param in range(self.q[core_param].param_no):
              current.append(
                  self.q[core_param].vi_return_param(approx_param)
              )
      return np.array(current)
  
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 4 for calling the BBVI obj's underlying member method code with example usage: obj.current_parameters() and returns: np