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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code: def out_degree_iter(self, nbunch=None, weight=None):
      if nx.__version__.startswith("1"):
          return super(DAG, self).out_degree_iter(nbunch, weight)
      else:
          return iter(self.out_degree(nbunch, weight))
  
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 9 for calling the DAG obj's underlying member method code with example usage: obj.out_degree_iter(nbunch, weight) and returns: super, iter