class_docstr: Mean Field approximation to the posterior where spherical Gaussian family
  is fitted to minimize KL divergence from True posterior. It is assumed
  that latent space variables are uncorrelated that is the main drawback
  of the method
class_docstr_tok: ["Mean","Field","approximation","to","the","posterior","where","spherical","Gaussian","family","is","fitted","to","minimize","KL","divergence","from","True","posterior",".","It","is","assumed","that","latent","space","variables","are","uncorrelated","that","is","the","main","drawback","of","the","method"]
code: def __init_group__(self, group):
      super().__init_group__(group)
      if not self._check_user_params():
          self.shared_params = self.create_shared_params(
              self._kwargs.get("start", None),
              self._kwargs.get("start_sigma", None),
          )
      self._finalize_init()
  
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 5 for calling the MeanFieldGroup obj's underlying member method code with example usage: obj.__init_group__(group) without return types