code: def __init__(
      self,
      n_clusters=8,
      init="k-means++",
      n_init=10,
      max_iter=300,
      tol=1e-4,
      precompute_distances="auto",
      verbose=0,
      random_state=None,
      copy_x=True,
      n_jobs=None,
      algorithm="auto",
  ):
      super(KMeans, self).__init__(
          n_clusters=n_clusters,
          init=init,
          max_iter=max_iter,
          tol=tol,
          precompute_distances=precompute_distances,
          n_init=n_init,
          verbose=verbose,
          random_state=random_state,
          copy_x=copy_x,
          n_jobs=n_jobs,
          algorithm=algorithm,
      )
  
imports: import numpy
  from scipy import sparse
  from sklearn.utils import check_array, check_random_state
  from sklearn.utils.sparsefuncs import mean_variance_axis
  from sklearn.utils.validation import _num_samples, check_is_fitted
  import warnings
  from sklearn.cluster import KMeans
  from sklearn.utils.extmath import row_norms
  import daal4py
  from .._device_offload import support_usm_ndarray
  from .._utils import PatchingConditionsChain, control_n_jobs, daal_check_version, getFPType, run_with_n_jobs
label_desc: Use image node_id 1 to create a new KMeans object from inherited base classes: KMeans_original with example: obj = KMeans(n_clusters, init, n_init, max_iter, tol, precompute_distances, verbose, random_state, copy_x, n_jobs, algorithm)