code: def __call__(self, x):
      dist = (
          x.pow(2).sum(1, keepdim=True)
          - 2 * torch.matmul(x, self.C)
          + self.Cnorm
      )
      return dist.argmin(dim=1).cpu().numpy()
  
file_path: audio/examples/hubert/utils/kmeans.py
imports: import logging
  from pathlib import Path
  from typing import Tuple
  import torch
  from sklearn.cluster import MiniBatchKMeans
  from torch import Tensor
  from .common_utils import _get_feat_lens_paths, _get_model_path
label_desc: Use image node_id 2 for calling the ApplyKmeans obj's underlying member method code with example usage: obj.__call__(x) and returns: dist