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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 median_kneighbour_distance(X, k=5):
 """ Calculate the median kneighbor distance. Find the distance between a set of random datapoints and their kth nearest neighbours. This is a heuristic for setting the kernel length scale. """

    N_all = X.shape[0]
    k = min(k, N_all)
    N_subset = min(N_all, 2000)
    sample_idx_train = np.random.permutation(N_all)[:N_subset]
    nn = neighbors.NearestNeighbors(k)
    nn.fit(X[sample_idx_train, :])
    d, idx = nn.kneighbors(X[sample_idx_train, :])
    return np.median(d[:, -1])