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: The unnormalized log posterior components (the quantity we want to approximate)
docstr_tok: ["The","unnormalized","log","posterior","components","(","the","quantity","we","want","to","approximate",")"]
code: def log_p(self, z):
      """
      The unnormalized log posterior components (the quantity we want to approximate)
      """
      return mb_log_p_posterior(z, self.neg_posterior, self.mini_batch)
  
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 2 for calling the BBVIM obj's underlying member method code with example usage: obj.log_p(z) and returns: mb_log_p_posterior