class_docstr: Base class for all Directed Graphical Models.
  
  Each node in the graph can represent either a random variable, `Factor`,
  or a cluster of random variables. Edges in the graph represent the
  dependencies between these.
  
  Parameters
  ----------
  data: input graph
      Data to initialize graph. If data=None (default) an empty graph is
      created. The data can be an edge list or any Networkx graph object.
  
  Examples
  --------
  Create an empty DAG with no nodes and no edges
  
  >>> from pgmpy.base import DAG
  >>> G = DAG()
  
  G can be grown in several ways:
  
  **Nodes:**
  
  Add one node at a time:
  
  >>> G.add_node(node='a')
  
  Add the nodes from any container (a list, set or tuple or the nodes
  from another graph).
  
  >>> G.add_nodes_from(nodes=['a', 'b'])
  
  **Edges:**
  
  G can also be grown by adding edges.
  
  Add one edge,
  
  >>> G.add_edge(u='a', v='b')
  
  a list of edges,
  
  >>> G.add_edges_from(ebunch=[('a', 'b'), ('b', 'c')])
  
  If some edges connect nodes not yet in the model, the nodes
  are added automatically. There are no errors when adding
  nodes or edges that already exist.
  
  **Shortcuts:**
  
  Many common graph features allow python syntax for speed reporting.
  
  >>> 'a' in G     # check if node in graph
  True
  >>> len(G)  # number of nodes in graph
  3
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docstr: Returns a dictionary of all ancestors of all the observed nodes including the
  node itself.
  
  Parameters
  ----------
  nodes: string, list-type
      name of all the observed nodes
  
  Examples
  --------
  >>> from pgmpy.base import DAG
  >>> model = DAG([('D', 'G'), ('I', 'G'), ('G', 'L'),
  ...                        ('I', 'L')])
  >>> model._get_ancestors_of('G')
  {'D', 'G', 'I'}
  >>> model._get_ancestors_of(['G', 'I'])
  {'D', 'G', 'I'}
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code: def _get_ancestors_of(self, nodes):
      """
      Returns a dictionary of all ancestors of all the observed nodes including the
      node itself.
  
      Parameters
      ----------
      nodes: string, list-type
          name of all the observed nodes
  
      Examples
      --------
      >>> from pgmpy.base import DAG
      >>> model = DAG([('D', 'G'), ('I', 'G'), ('G', 'L'),
      ...                        ('I', 'L')])
      >>> model._get_ancestors_of('G')
      {'D', 'G', 'I'}
      >>> model._get_ancestors_of(['G', 'I'])
      {'D', 'G', 'I'}
      """
      if not isinstance(nodes, (list, tuple)):
          nodes = [nodes]
  
      for node in nodes:
          if node not in self.nodes():
              raise ValueError(f"Node {node} not in not in graph")
  
      ancestors_list = set()
      nodes_list = set(nodes)
      while nodes_list:
          node = nodes_list.pop()
          if node not in ancestors_list:
              nodes_list.update(self.predecessors(node))
          ancestors_list.add(node)
      return ancestors_list
  
imports: import itertools
  import networkx
  import numpy
  from pgmpy.base import UndirectedGraph
  from pgmpy.global_vars import logger
  from pgmpy.independencies import Independencies
label_desc: Use image node_id 21 for calling the DAG obj's underlying member method code with example usage: obj._get_ancestors_of(nodes) and returns: ancestors_list