class_docstr: Implements the Minimum Degree heuristic.
  
  The heuristic chooses the nodes according to their degree
  (number of neighbors), i.e., first the node with the lowest degree is
  chosen, then the graph is updated and the corresponding node is
  removed. Next, a new node with the lowest degree is chosen, and so on.
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code: def best_node(self, graph):
      # update nodes in self._update_nodes
      for n in self._update_nodes:
          # insert changed degrees into degreeq
          heappush(self._degreeq, (len(graph[n]), next(self.count), n))
  
      # get the next valid (minimum degree) node
      while self._degreeq:
          (min_degree, _, elim_node) = heappop(self._degreeq)
          if (
              elim_node not in graph
              or len(graph[elim_node]) != min_degree
          ):
              # outdated entry in degreeq
              continue
          elif min_degree == len(graph) - 1:
              # fully connected: abort condition
              return None
  
          # remember to update nodes in the heap before getting the next node
          self._update_nodes = graph[elim_node]
          return elim_node
  
      # the heap is empty: abort
      return None
  
file_path: networkx/networkx/algorithms/approximation/treewidth.py
imports: import itertools
  import sys
  from heapq import heapify, heappop, heappush
  import networkx
  from networkx.utils import not_implemented_for
label_desc: Use image node_id 2 for calling the MinDegreeHeuristic obj's underlying member method code with example usage: obj.best_node(graph) and returns: None, elim_node, None