K-Means Clustering: Compute Centroids After N Iterations

apple Implement a k_means(clients, k, n) function. Given a 2D array clients with shape (N, 2) representing clients, an integer k representing the number of clusters, and an integer n representing the number of Assignment and Update steps. The function should correctly cluster the data and output the centroids of each cluster after n steps. Input: clients: A 2D array with shape (N, 2). k: An integer representing the number of clusters. n: An integer representing the number of Assignment and Update steps. Output: Returns a list containing the centroids of each cluster. Example: k_means([[1.0, 2.0], [2.0, 1.0], [3.0, 5.0], [8.0, 7.0]], 2, 10) Assume the output is [centroid1, centroid2]. Example Input [[1.0, 2.0], [2.0, 1.0], [3.0, 5.0], [8.0, 7.0]] 2 10