class_docstr: This is an inference class for performing Causal Inference over Bayesian
  Networks or Structural Equation Models.
  
  Parameters
  ----------
  model: pgmpy.base.DAG | pgmpy.models.BayesianNetwork
      The model that we'll perform inference over.
  
  set_nodes: list[node:str] or None
      A list (or set/tuple) of nodes in the Bayesian Network which have been
      set to a specific value per the do-operator.
  
  Examples
  --------
  Create a small Bayesian Network.
  
  >>> from pgmpy.models import BayesianNetwork
  >>> game = BayesianNetwork([('X', 'A'),
  ...                         ('A', 'Y'),
  ...                         ('A', 'B')])
  
  Load the graph into the CausalInference object to make causal queries.
  
  >>> from pgmpy.inference.CausalInference import CausalInference
  >>> inference = CausalInference(game)
  >>> inference.get_all_backdoor_adjustment_sets(X="X", Y="Y")
  >>> inference.get_all_frontdoor_adjustment_sets(X="X", Y="Y")
  
  References
  ----------
  'Causality: Models, Reasoning, and Inference' - Judea Pearl (2000)
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code: def __repr__(self):
      variables = ", ".join(map(str, sorted(self.observed_variables)))
      return f"{self.__class__.__name__}({variables})"
  
imports: from collections.abc import Iterable
  from itertools import chain, product
  import networkx
  import numpy
  from tqdm.auto import tqdm
  from pgmpy import config
  from pgmpy.estimators.LinearModel import LinearEstimator
  from pgmpy.factors.discrete import DiscreteFactor
  from pgmpy.models import BayesianNetwork
  from pgmpy.utils.sets import _powerset, _variable_or_iterable_to_set
label_desc: Use image node_id 2 for calling the CausalInference obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, variables, str