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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 nn_x(self, x, k=1, radius=np.inf, eps=0.0, p=2):
 """Find the k nearest neighbors of x in the observed input data @see Databag.nn() for argument description @return distance and indexes of found nearest neighbors. """

        assert len(x) == self.dim_x
        k_x = min(k, self.size)
        # Because linear models requires x vector to be extended to [1.0]+x
        # to accomodate a constant, we store them that way.
        return self._nn(DATA_X, x, k=k_x, radius=radius, eps=eps, p=p)