class_docstr: Base class for Markov Model.
  
  A MarkovNetwork stores nodes and edges with potentials
  
  MarkovNetwork holds undirected edges.
  
  Parameters
  ----------
  data : input graph
      Data to initialize graph.  If data=None (default) an empty
      graph is created.  The data can be an edge list, or any
      NetworkX graph object.
  
  Examples
  --------
  Create an empty Markov Model with no nodes and no edges.
  
  >>> from pgmpy.models import MarkovNetwork
  >>> G = MarkovNetwork()
  
  G can be grown in several ways.
  
  **Nodes:**
  
  Add one node at a time:
  
  >>> G.add_node('a')
  
  Add the nodes from any container (a list, set or tuple or the nodes
  from another graph).
  
  >>> G.add_nodes_from(['a', 'b'])
  
  **Edges:**
  
  G can also be grown by adding edges.
  
  Add one edge,
  
  >>> G.add_edge('a', 'b')
  
  a list of edges,
  
  >>> G.add_edges_from([('a', 'b'), ('b', 'c')])
  
  If some edges connect nodes not yet in the model, the nodes
  are added automatically.  There are no errors when adding
  nodes or edges that already exist.
  
  **Shortcuts:**
  
  Many common graph features allow python syntax for speed reporting.
  
  >>> 'a' in G     # check if node in graph
  True
  >>> len(G)  # number of nodes in graph
  3
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docstr: Check the model for various errors. This method checks for the following
  errors -
  
  * Checks if the cardinalities of all the variables are consistent across all the factors.
  * Factors are defined for all the random variables.
  
  Returns
  -------
  check: boolean
      True if all the checks are passed
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code: def check_model(self):
      """
      Check the model for various errors. This method checks for the following
      errors -
  
      * Checks if the cardinalities of all the variables are consistent across all the factors.
      * Factors are defined for all the random variables.
  
      Returns
      -------
      check: boolean
          True if all the checks are passed
      """
      cardinalities = self.get_cardinality()
      for factor in self.factors:
          for variable, cardinality in zip(
              factor.scope(), factor.cardinality
          ):
              if cardinalities[variable] != cardinality:
                  raise ValueError(
                      f"Cardinality of variable {variable} not matching among factors"
                  )
              if len(self.nodes()) != len(cardinalities):
                  raise ValueError(
                      "Factors for all the variables not defined"
                  )
          for var1, var2 in itertools.combinations(factor.variables, 2):
              if var2 not in self.neighbors(var1):
                  raise ValueError(
                      "DiscreteFactor inconsistent with the model."
                  )
      return True
  
imports: import itertools
  from collections import defaultdict
  import networkx
  import numpy
  from networkx.algorithms.components import connected_components
  from pgmpy.base import UndirectedGraph
  from pgmpy.factors import factor_product
  from pgmpy.factors.discrete import DiscreteFactor
  from pgmpy.independencies import Independencies
label_desc: Use image node_id 8 for calling the MarkovNetwork obj's underlying member method code with example usage: obj.check_model() and returns: True