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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 query(self, x, k=1, p=2, distance_upper_bound=np.inf):
 """Query the kd-tree for nearest neighbors Parameters x : array_like, last dimension self.m An array of points to query. k : int, optional The number of nearest neighbors to return. eps : nonnegative float, optional Return approximate nearest neighbors; the kth returned value is guaranteed to be no further than (1+eps) times the distance to the real kth nearest neighbor. p : float, 1<=p<=infinity, optional Which Minkowski p-norm to use. 1 is the sum-of-absolute-values "Manhattan" distance 2 is the usual Euclidean distance infinity is the maximum-coordinate-difference distance distance_upper_bound : nonnegative float, optional Return only neighbors within this distance. This is used to prune tree searches, so if you are doing a series of nearest-neighbor queries, it may help to supply the distance to the nearest neighbor of the most recent point. Returns ------- d : float or array of floats The distances to the nearest neighbors. If x has shape tuple+(self.m,), then d has shape tuple if k is one, or tuple+(k,) if k is larger than one. Missing neighbors (e.g. when k > n or distance_upper_bound is given) are indicated with infinite distances. If k is None, then d is an object array of shape tuple, containing lists of distances. In either case the hits are sorted by distance (nearest first). i : tuple(int, int) or array of tuple(int, int) The locations of the neighbors in self.data. Locations are given by tuples of (traj_i, frame_i) Examples -------- (array([ 0.0034, 0.0102]), array([[ 0, 410], [ 1, 670]])) (0.0034, array([ 0, 410])) """

        cdists, cinds = self._kdtree.query(x, k, p, distance_upper_bound)
        return cdists, self._split_indices(cinds)