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 __init__(self, graph):
      self._graph = graph
  
      # nodes that have to be updated in the heap before each iteration
      self._update_nodes = []
  
      self._degreeq = (
          []
      )  # a heapq with 3-tuples (degree,unique_id,node)
      self.count = itertools.count()
  
      # build heap with initial degrees
      for n in graph:
          self._degreeq.append((len(graph[n]), next(self.count), n))
      heapify(self._degreeq)
  
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 1 to create a new MinDegreeHeuristic object  with example: obj = MinDegreeHeuristic(graph)