code: def train(
      model,
      train_set,
      ssl_model,
      ssl_layer_num,
      kmeans_batch_size=1000,
      device="cpu",
  ):
      """Train a  Kmeans model .
  
      Arguments
      ---------
          model : MiniBatchKMeans
              The initial kmeans model for training.
          train_set : Dataloader
              Batches of tarining data.
          ssl_model
              SSL-model used to  extract features used for clustering.
          ssl_layer_num : int
              Specify output of which layer of the ssl_model should be used.
          device
              CPU or  GPU.
          kmeans_batch_size : int
              Size of the mini batches.
      """
      logger.info("Start training kmeans model.")
      features_list = []
      with tqdm(
          train_set,
          dynamic_ncols=True,
      ) as t:
          for batch in t:
              # train a kmeans model on a single batch if  features_list reaches the kmeans_batch_size.
              if len(features_list) >= kmeans_batch_size:
                  model = model.fit(features_list)
                  features_list = []
              # extract features from the SSL model
              accumulate_and_extract_features(
                  batch, features_list, ssl_model, ssl_layer_num, device
              )
  
imports: import os
  import logging
  from tqdm.contrib import tqdm
  import joblib
label_desc: Use image node_id 3 for calling a global function with example usage: train(model, train_set, ssl_model, ssl_layer_num, kmeans_batch_size, device) without return types