code: import numpy as np
  from mpi4py import MPI
  
  
  class Kmeans:
      def __init__(self, k=3, num_iterations=100, seed=42):
          self.k = k
          self.num_iterations = num_iterations
          self.centorids = None
          self.dim = None
          self.n = None
  
          np.random.seed(seed)
  
      def train(self, X, parallel=False):
          if parallel:
              pass
          else:
              return self._train_standalone(X)
  
      def _init_distiution(self, args=None):
          self.args = args
          self.comm = MPI.COMM_WORLD
          self.rank = self.comm.Get_rank()
          self.size = self.comm.Get_size()
  
      def _em_standalone(self, X):
          # E-step
          distance = np.zeros((self.k, self.n))
          for cluster_id in range(self.k):
              distance[cluster_id, :] = np.linalg.norm(
                  X - self.centorids[cluster_id, :], axis=1
              )
          pred = np.argmin(distance, axis=0)
  
          # M-step
          for cluster_id in range(self.k):
              self.centorids[cluster_id, :] = np.mean(X[pred == cluster_id, :], axis=0)
  
          return pred
  
      def _train_standalone(self, X):
          self.n = X.shape[0]
          self.dim = X.shape[1]
          self.centorids = np.random.normal(0, 1, (self.k, self.dim))
  
          for _ in range(self.num_iterations):
              pred = self._em_standalone(X)
  
          return pred
  
apis: numpy.zeros numpy.argmin numpy.random.seed numpy.random.normal numpy.linalg.norm numpy.mean