def METHOD_NAME(chunks):
    results = defaultdict(lambda: [])
    n_features = 50000
    means = np.array(
        [
            [1, 1],
            [-1, -1],
            [1, -1],
            [-1, 1],
            [0.5, 0.5],
            [0.75, -0.5],
            [-1, 0.75],
            [1, 0],
        ]
    )
    X = np.empty((0, 2))
    for i in range(8):
        X = np.r_[X, means[i] + 0.8 * np.random.randn(n_features, 2)]
    max_it = len(chunks)
    it = 0
    for chunk in chunks:
        it += 1
        print("==============================")
        print("Iteration %03d of %03d" % (it, max_it))
        print("==============================")
        print()
        print("Fast K-Means")
        tstart = time()
        mbkmeans = MiniBatchKMeans(init="k-means++", n_clusters=8, batch_size=chunk)
        mbkmeans.fit(X)
        delta = time() - tstart
        print("Speed: %0.3fs" % delta)
        print("Inertia: %0.3fs" % mbkmeans.inertia_)
        print()
        results["MiniBatchKMeans Speed"].append(delta)
        results["MiniBatchKMeans Quality"].append(mbkmeans.inertia_)
    return results