docstr: The cost of eliminating a node is the product of weights, domain cardinality,
  of its neighbors.
docstr_tok: ["The","cost","of","eliminating","a","node","is","the","product","of","weights",",","domain","cardinality",",","of","its","neighbors","."]
code: def cost(self, node):
      """
      The cost of eliminating a node is the product of weights, domain cardinality,
      of its neighbors.
      """
      return np.prod(
          [
              self.bayesian_model.get_cardinality(neig_node)
              for neig_node in self.moralized_model.neighbors(node)
          ]
      )
  
imports: from abc import abstractmethod
  from itertools import combinations
  import numpy
  from tqdm.auto import tqdm
  from pgmpy import config
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 1 for calling the MinWeight obj's underlying member method code with example usage: obj.cost(node) and returns: np