docstr: Returns the width (integer) of the induced graph formed by running Variable Elimination on the network.
  The width is the defined as the number of nodes in the largest clique in the graph minus 1.
  
  Parameters
  ----------
  elimination_order: list, array like
      List of variables in the order in which they are to be eliminated.
  
  Examples
  --------
  >>> import numpy as np
  >>> import pandas as pd
  >>> from pgmpy.models import BayesianNetwork
  >>> from pgmpy.inference import VariableElimination
  >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
  ...                       columns=['A', 'B', 'C', 'D', 'E'])
  >>> model = BayesianNetwork([('A', 'B'), ('C', 'B'), ('C', 'D'), ('B', 'E')])
  >>> model.fit(values)
  >>> inference = VariableElimination(model)
  >>> inference.induced_width(['C', 'D', 'A', 'B', 'E'])
  3
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code: def induced_width(self, elimination_order):
      """
      Returns the width (integer) of the induced graph formed by running Variable Elimination on the network.
      The width is the defined as the number of nodes in the largest clique in the graph minus 1.
  
      Parameters
      ----------
      elimination_order: list, array like
          List of variables in the order in which they are to be eliminated.
  
      Examples
      --------
      >>> import numpy as np
      >>> import pandas as pd
      >>> from pgmpy.models import BayesianNetwork
      >>> from pgmpy.inference import VariableElimination
      >>> values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
      ...                       columns=['A', 'B', 'C', 'D', 'E'])
      >>> model = BayesianNetwork([('A', 'B'), ('C', 'B'), ('C', 'D'), ('B', 'E')])
      >>> model.fit(values)
      >>> inference = VariableElimination(model)
      >>> inference.induced_width(['C', 'D', 'A', 'B', 'E'])
      3
      """
      induced_graph = self.induced_graph(elimination_order)
      return (
          max(
              (len(clique) for clique in nx.find_cliques(induced_graph))
          )
          - 1
      )
  
imports: import copy
  import itertools
  import networkx
  import numpy
  from opt_einsum import contract
  from tqdm.auto import tqdm
  from pgmpy import config
  from pgmpy.factors import factor_product
  from pgmpy.factors.discrete import DiscreteFactor
  from pgmpy.inference import Inference
  from pgmpy.inference.EliminationOrder import MinFill, MinNeighbors, MinWeight, WeightedMinFill
  from pgmpy.models import BayesianNetwork, DynamicBayesianNetwork, JunctionTree, MarkovNetwork
  from pgmpy.utils import compat_fns
label_desc: Use image node_id 8 for calling the VariableElimination obj's underlying member method code with example usage: obj.induced_width(elimination_order) and returns: unknown