class_docstr: Class to represent Naive Bayes. Naive Bayes is a special case of Bayesian Model
  where the only edges in the model are from the feature variables to the dependent variable.
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docstr: Adds edges to the model.
  
  Each tuple of the form (u, v) in ebunch adds a new edge in the model.
  Since there can only be one dependent variable in a Naive Bayes model, `u` should
  be the same for each tuple in `ebunch`.
  
  Parameters
  ----------
  ebunch: list (array-like)
      A list of tuples of the form (u, v) representing an edge from u to v.
  
  Returns
  -------
  None
  
  Examples
  --------
  >>> from pgmpy.models import NaiveBayes
  >>> G = NaiveBayes()
  >>> G.add_nodes_from(['a', 'b', 'c'])
  >>> G.add_edges_from([('a', 'b'), ('a', 'c')])
  >>> G.edges()
  OutEdgeView([('a', 'b'), ('a', 'c')])
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code: def add_edges_from(self, ebunch):
      """
      Adds edges to the model.
  
      Each tuple of the form (u, v) in ebunch adds a new edge in the model.
      Since there can only be one dependent variable in a Naive Bayes model, `u` should
      be the same for each tuple in `ebunch`.
  
      Parameters
      ----------
      ebunch: list (array-like)
          A list of tuples of the form (u, v) representing an edge from u to v.
  
      Returns
      -------
      None
  
      Examples
      --------
      >>> from pgmpy.models import NaiveBayes
      >>> G = NaiveBayes()
      >>> G.add_nodes_from(['a', 'b', 'c'])
      >>> G.add_edges_from([('a', 'b'), ('a', 'c')])
      >>> G.edges()
      OutEdgeView([('a', 'b'), ('a', 'c')])
      """
      for u, v in ebunch:
          self.add_edge(u, v)
  
file_path: pgmpy/pgmpy/models/NaiveBayes.py
imports: from pgmpy.independencies import Independencies
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 3 for calling the NaiveBayes obj's underlying member method code with example usage: obj.add_edges_from(ebunch) without return types