class_docstr: Black Box Variational Inference - minibatch
  
  Parameters
  ----------
  neg_posterior : function
      posterior function
  
  full_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
  
  mini_batch : int
      Mini batch size
  
  record_elbo : boolean
      Whether to record the ELBO
  
  quiet_progress : boolean
      Whether to print progress or stay quiet
docstr: Prints the current ELBO at every decile of total iterations
docstr_tok: ["Prints","the","current","ELBO","at","every","decile","of","total","iterations"]
code: def print_progress(self, i, current_params):
      """
      Prints the current ELBO at every decile of total iterations
      """
      for split in range(1, 11):
          if i == (round(self.iterations / 10 * split) - 1):
              post = -self.full_neg_posterior(current_params)
              approx = self.create_normal_logq(current_params)
              diff = post - approx
              if not self.quiet_progress:
                  print(
                      str(split)
                      + "0% done : ELBO is "
                      + str(diff)
                      + ", p(y,z) is "
                      + str(post)
                      + ", q(z) is "
                      + str(approx)
                  )
  
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 BBVIM obj's underlying member method code with example usage: obj.print_progress(i, current_params) without return types