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: Returns an instance of Independencies containing the local independencies
  of each of the variables.
  
  
  Parameters
  ----------
  variables: str or array like
      variables whose local independencies are to found.
  
  Examples
  --------
  >>> from pgmpy.models import NaiveBayes
  >>> model = NaiveBayes()
  >>> model.add_edges_from([('a', 'b'), ('a', 'c'), ('a', 'd')])
  >>> ind = model.local_independencies('b')
  >>> ind
  (b ⟂ d, c | a)
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code: def local_independencies(self, variables):
      """
      Returns an instance of Independencies containing the local independencies
      of each of the variables.
  
  
      Parameters
      ----------
      variables: str or array like
          variables whose local independencies are to found.
  
      Examples
      --------
      >>> from pgmpy.models import NaiveBayes
      >>> model = NaiveBayes()
      >>> model.add_edges_from([('a', 'b'), ('a', 'c'), ('a', 'd')])
      >>> ind = model.local_independencies('b')
      >>> ind
      (b \u27C2 d, c | a)
      """
      independencies = Independencies()
      for variable in (
          [variables] if isinstance(variables, str) else variables
      ):
          if variable != self.dependent:
              independencies.add_assertions(
                  [
                      variable,
                      list(set(self.features) - set(variable)),
                      self.dependent,
                  ]
              )
      return independencies
  
file_path: pgmpy/pgmpy/models/NaiveBayes.py
imports: from pgmpy.independencies import Independencies
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 6 for calling the NaiveBayes obj's underlying member method code with example usage: obj.local_independencies(variables) and returns: independencies