class_docstr: Full Rank approximation to the posterior where Multivariate Gaussian family
  is fitted to minimize KL divergence from True posterior. In contrast to
  MeanField approach correlations between variables are taken in account. The
  main drawback of the method is computational cost.
class_docstr_tok: ["Full","Rank","approximation","to","the","posterior","where","Multivariate","Gaussian","family","is","fitted","to","minimize","KL","divergence","from","True","posterior",".","In","contrast","to","MeanField","approach","correlations","between","variables","are","taken","in","account",".","The","main","drawback","of","the","method","is","computational","cost","."]
code: def std(self):
      return pt.sqrt(pt.diag(self.cov))
  
imports: from typing import Optional
  import numpy
  import pytensor
  from arviz import InferenceData
  from pytensor import tensor
  from pytensor.graph.basic import Variable
  from pytensor.graph.replace import graph_replace
  from pytensor.tensor.variable import TensorVariable
  import pymc
  from pymc.blocking import DictToArrayBijection
  from pymc.distributions.dist_math import rho2sigma
  from pymc.util import makeiter
  from pymc.variational import opvi
  from pymc.variational.opvi import Approximation, Group, NotImplementedInference, _known_scan_ignored_inputs, node_property
label_desc: Use image node_id 6 for calling the FullRankGroup obj's underlying member method code with example usage: obj.std() and returns: pt