code: def cluster_features(cfg: AttrDict):
      assert is_faiss_available(), (
          "Please install faiss using conda install faiss-gpu -c pytorch "
          "if using conda or pip install faiss-gpu"
      )
      import faiss
  
      num_clusters = cfg.CLUSTERFIT.NUM_CLUSTERS
      cluster_backend = cfg.CLUSTERFIT.CLUSTER_BACKEND
      data_split = cfg.CLUSTERFIT.FEATURES.DATA_PARTITION
  
      # Step 1: get a sub-sample of the extract features on the whole dataset
      # in order to compute the centroids
      feature_data = get_data_features_for_k_means(cfg)
      features = feature_data["features"]
      assert (
          features.ndim == 2
      ), f"Invalid feature shape: {features.shape}"
      assert (
          features.dtype == np.float32
      ), "Features are not float32 type"
      logging.info(f"Loaded features: {features.shape}")
  
      # Step 2: normalize the features and apply dimensionality reduction
      logging.info("Normalizing the features...")
      feat_norm = np.linalg.norm(features, axis=1) + 1e-5
      features = features / feat_norm[:, np.newaxis]
      with_dimensionality_reduction = (
          cfg.CLUSTERFIT.FEATURES.DIMENSIONALITY_REDUCTION > 0
      )
      if with_dimensionality_reduction:
          pca = PCA(
              n_components=cfg.CLUSTERFIT.FEATURES.DIMENSIONALITY_REDUCTION
          )
          features = pca.fit_transform(features)
          features = np.ascontiguousarray(features)
          features_dim = (
              cfg.CLUSTERFIT.FEATURES.DIMENSIONALITY_REDUCTION
          )
      else:
          pca = None
          features_dim = features.shape[1]
  
      # Step 3: compute the centroids for the sub-sampled features
      logging.info(
          f"Clustering {features.shape[0]} features in {num_clusters} clusters..."
      )
      assert (
          cluster_backend == "faiss"
      ), "Only faiss clustering is supported currently"
      use_gpu = torch.cuda.device_count() > 0
      num_iter = cfg.CLUSTERFIT.NUM_ITER
      kmeans = faiss.Kmeans(
          features.shape[1],
          num_clusters,
          niter=num_iter,
          verbose=True,
          gpu=use_gpu,
      )
      kmeans.train(features)
  
      # Step 4: compute the cluster assignment for each of the features of the dataset
      # by streaming through the features (to avoid OOM) and save clustering data
      # and hard cluster labels for the images
      _create_dataset_split(cfg, data_split, features_dim, kmeans, pca)
      if cfg.CLUSTERFIT.FEATURES.TEST_PARTITION:
          test_split = cfg.CLUSTERFIT.FEATURES.TEST_PARTITION
          _create_dataset_split(
              cfg, test_split, features_dim, kmeans, pca
          )
      logging.info("All Done!")
  
imports: import logging
  import os
  import sys
  from argparse import Namespace
  from typing import Any, List, Optional
  import numpy
  import torch
  from iopath.common.file_io import g_pathmgr
  from vissl.config import AttrDict
  from vissl.data import build_dataset
  from vissl.hooks import default_hook_generator
  from vissl.utils.checkpoint import get_checkpoint_folder
  from vissl.utils.distributed_launcher import launch_distributed
  from vissl.utils.env import set_env_vars, setup_path_manager
  from vissl.utils.extract_features_utils import ExtractedFeaturesLoader
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.io import save_file
  from vissl.utils.logger import setup_logging, shutdown_logging
  from vissl.utils.misc import is_faiss_available, set_seeds
  from vissl.utils.pca import PCA
label_desc: Use image node_id 3 for calling a global function with example usage: cluster_features(cfg) without return types