class_docstr: _EfficientKMeans is primarily used by palettization to perform a k-means operation. This class also has an in-house
  implementation of k-means, called ``kmeans_pp`` which runs entirely on GPU and is ~10x faster than sklearn's API.
code: def cost(self, i: int, j: int, new_cluster_cost: float):
      if i > j:
          cur_cost = 0
      else:
          size = j - i + 1
          sum_i_j = self.prefix_x[j] - (
              self.prefix_x[i - 1] if i >= 1 else 0
          )
          sum2_i_j = self.prefix_x2[j] - (
              self.prefix_x2[i - 1] if i >= 1 else 0
          )
          mean_i_j = sum_i_j / size
          cc_i_j = -mean_i_j * mean_i_j * size + sum2_i_j
  
          if cc_i_j < 0:
              cc_i_j = 0
  
          cur_cost = (
              cc_i_j * (1 - self.tol) + new_cluster_cost * self.tol
          )
  
      return cur_cost
  
file_path: coremltools/coremltools/optimize/torch/palettization/_efficient_kmeans.py
imports: import numpy
  import torch
  import torch.distributed
label_desc: Use image node_id 5 for calling the _EfficientKMeans obj's underlying member method code with example usage: obj.cost(i, j, new_cluster_cost) and returns: cur_cost