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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def is_nash(self, action_profile, tol=None):
 """ Return True if `action_profile` is a Nash equilibrium. Parameters action_profile : array_like(int or array_like(float)) An array of N objects, where each object must be an integer (pure action) or an array of floats (mixed action). tol : scalar(float) Tolerance level used in determining best responses. If None, default to each player's `tol` attribute value. Returns ------- bool True if `action_profile` is a Nash equilibrium; False otherwise. """

        if self.N == 2:
            for i, player in enumerate(self.players):
                own_action, opponent_action = \
                    action_profile[i], action_profile[1-i]
                if not player.is_best_response(own_action, opponent_action,
                                               tol):
                    return False

        elif self.N >= 3:
            for i, player in enumerate(self.players):
                own_action = action_profile[i]
                opponents_actions = \
                    tuple(action_profile[i+1:]) + tuple(action_profile[:i])

                if not player.is_best_response(own_action, opponents_actions,
                                               tol):
                    return False

        else:  # Trivial case with self.N == 1
            if not self.players[0].is_best_response(action_profile[0], None,
                                                    tol):
                return False

        return True