code: def make_node_func(variable_name, conditions):
      # We will enforce the following
      # convention.
      # The ordering of arguments will
      # be firstly the parent variables
      # in alphabetical order, followed
      # always by the child variable
      tt = {}
      domain = set()
      for givens, conditionals in conditions:
          key = []
          for parent_name, val in sorted(givens):
              key.append((parent_name, val))
          # Now we will sort the
          # key before we add the child
          # node.
          # key.sort(key=lambda x: x[0])
  
          # Now for each value in
          # the conditional probabilities
          # we will add a new key
          for value, prob in list(conditionals.items()):
              key_ = tuple(key + [(variable_name, value)])
              domain.add(value)
              tt[key_] = prob
  
      argspec = [k[0] for k in key_]
  
      def node_func(*args):
          key = []
          for arg, val in zip(argspec, args):
              key.append((arg, val))
          return tt[tuple(key)]
  
      node_func.argspec = argspec
      node_func._domain = domain
      node_func.__name__ = "f_" + variable_name
      return node_func
  
imports: import copy
  import heapq
  import logging
  from collections import defaultdict
  from io import StringIO
  from itertools import combinations, product
  from random import choice, random
  from .exceptions import VariableNotInGraphError, VariableValueNotInDomainError
  from .graph import Node, UndirectedGraph, UndirectedNode, connect
  from .utils import get_args, get_original_factors
label_desc: Use image node_id 3 for calling a global function with example usage: make_node_func(variable_name, conditions) and returns: node_func, tt