class_docstr: **Full Rank Automatic Differentiation Variational Inference (ADVI)**
  
  Parameters
  ----------
  model: :class:`pymc.Model`
      PyMC model for inference
  random_seed: None or int
  start: `dict[str, np.ndarray]` or `StartDict`
      starting point for inference
  
  References
  ----------
  -   Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A.,
      and Blei, D. M. (2016). Automatic Differentiation Variational
      Inference. arXiv preprint arXiv:1603.00788.
  
  -   Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016
      Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI
      approximateinference.org/accepted/RoederEtAl2016.pdf
  
  -   Kingma, D. P., & Welling, M. (2014).
      Auto-Encoding Variational Bayes. stat, 1050, 1.
code: def __init__(self, *args, **kwargs):
      super().__init__(FullRank(*args, **kwargs))
  
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 1 to create a new FullRankADVI object from inherited base classes: KLqp with example: obj = FullRankADVI()