class_docstr: **Base class for Variational Inference**
  
  Communicates Operator, Approximation and Test Function to build Objective Function
  
  Parameters
  ----------
  op : Operator class    #:class:`~pymc.variational.operators`
  approx : Approximation class or instance    #:class:`~pymc.variational.approximations`
  tf : TestFunction instance  #?
  model : Model
      PyMC Model
  kwargs : kwargs passed to :class:`Operator` #:class:`~pymc.variational.operators`, optional
docstr: Refine the solution using the last compiled step function
docstr_tok: ["Refine","the","solution","using","the","last","compiled","step","function"]
code: def refine(self, n, progressbar=True):
      """Refine the solution using the last compiled step function"""
      if self.state is None:
          raise TypeError("Need to call `.fit` first")
      i, step, callbacks, score = self.state
      if progressbar:
          progress = progress_bar(range(n), display=progressbar)
      else:
          progress = range(
              n
          )  # This is a guess at what progress_bar(n) does.
      if score:
          state = self._iterate_with_loss(
              i, n, step, progress, callbacks
          )
      else:
          state = self._iterate_without_loss(
              i, n, step, progress, callbacks
          )
      self.state = state
  
imports: import collections
  import logging
  import warnings
  import numpy
  from fastprogress.fastprogress import progress_bar
  import pymc
  from pymc.variational import test_functions
  from pymc.variational.approximations import Empirical, FullRank, MeanField
  from pymc.variational.operators import KL, KSD
label_desc: Use image node_id 7 for calling the Inference obj's underlying member method code with example usage: obj.refine(n, progressbar) without return types