code: def k_means(
      X,
      n_clusters,
      sample_weight=None,
      init="k-means||",
      n_init=10,
      max_iter=300,
      verbose=False,
      tol=1e-4,
      random_state=None,
      copy_x=True,
      algorithm="auto",
      oversampling_factor=2,
      init_iter=5,
      return_n_iter=False,
  ):
      """K-means clustering algorithm.
  
      Parameters
      ----------
      X : Tensor, shape (n_samples, n_features)
          The observations to cluster. It must be noted that the data
          will be converted to C ordering, which will cause a memory copy
          if the given data is not C-contiguous.
  
      n_clusters : int
          The number of clusters to form as well as the number of
          centroids to generate.
  
      sample_weight : array-like, shape (n_samples,), optional
          The weights for each observation in X. If None, all observations
          are assigned equal weight (default: None)
  
      init : {'k-means++', 'k-means||', 'random', or tensor, or a callable}, optional
          Method for initialization, default to 'k-means||':
  
          'k-means++' : selects initial cluster centers for k-mean
          clustering in a smart way to speed up convergence. See section
          Notes in k_init for more details.
  
          'k-means||': scalable k-means++.
  
          'random': choose k observations (rows) at random from data for
          the initial centroids.
  
          If an ndarray is passed, it should be of shape (n_clusters, n_features)
          and gives the initial centers.
  
          If a callable is passed, it should take arguments X, k and
          and a random state and return an initialization.
  
      n_init : int, optional, default: 10
          Number of time the k-means algorithm will be run with different
          centroid seeds. The final results will be the best output of
          n_init consecutive runs in terms of inertia.
  
      max_iter : int, optional, default 300
          Maximum number of iterations of the k-means algorithm to run.
  
      verbose : boolean, optional
          Verbosity mode.
  
      tol : float, optional
          The relative increment in the results before declaring convergence.
  
      random_state : int, RandomState instance or None (default)
          Determines random number generation for centroid initialization. Use
          an int to make the randomness deterministic.
          See :term:`Glossary <random_state>`.
  
      copy_x : bool, optional
          When pre-computing distances it is more numerically accurate to center
          the data first.  If copy_x is True (default), then the original data is
          not modified, ensuring X is C-contiguous.  If False, the original data
          is modified, and put back before the function returns, but small
          numerical differences may be introduced by subtracting and then adding
          the data mean, in this case it will also not ensure that data is
          C-contiguous which may cause a significant slowdown.
  
      algorithm : "auto", "full" or "elkan", default="auto"
          K-means algorithm to use. The classical EM-style algorithm is "full".
          The "elkan" variation is more efficient by using the triangle
          inequality, but currently doesn't support sparse data. "auto" chooses
          "elkan" for dense data and "full" for sparse data.
  
      oversampling_factor: int, default=2
          Only work for kmeans||, used in each iteration in kmeans||.
  
      init_iter: int, default=5
          Only work for kmeans||, indicates how may iterations required.
  
      return_n_iter : bool, optional
          Whether or not to return the number of iterations.
  
      Returns
      -------
      centroid : float ndarray with shape (k, n_features)
          Centroids found at the last iteration of k-means.
  
      label : integer ndarray with shape (n_samples,)
          label[i] is the code or index of the centroid the
          i'th observation is closest to.
  
      inertia : float
          The final value of the inertia criterion (sum of squared distances to
          the closest centroid for all observations in the training set).
  
      best_n_iter : int
          Number of iterations corresponding to the best results.
          Returned only if `return_n_iter` is set to True.
      """
  
      est = KMeans(
          n_clusters=n_clusters,
          init=init,
          n_init=n_init,
          max_iter=max_iter,
          verbose=verbose,
          tol=tol,
          random_state=random_state,
          copy_x=copy_x,
          algorithm=algorithm,
          oversampling_factor=oversampling_factor,
          init_iter=init_iter,
      ).fit(X, sample_weight=sample_weight)
      if return_n_iter:
          return (
              est.cluster_centers_,
              est.labels_,
              est.inertia_,
              est.n_iter_,
          )
      else:
          return est.cluster_centers_, est.labels_, est.inertia_
  
imports: import warnings
  import numpy
  from sklearn.base import ClusterMixin, TransformerMixin
  from sklearn.exceptions import ConvergenceWarning
  from ...None import tensor
  from ...tensor.utils import check_random_state
  from ..base import BaseEstimator
  from ..metrics.pairwise import euclidean_distances
  from ..utils.extmath import row_norms
  from ..utils.validation import _check_sample_weight, check_array, _num_samples, check_is_fitted
  from ._k_means_common import _inertia
  from ._k_means_elkan_iter import init_bounds, elkan_iter
  from ._k_means_init import _k_init, _scalable_k_init
  from ._k_means_lloyd_iter import lloyd_iter
label_desc: Use image node_id 4 for calling a global function with example usage: k_means(X, n_clusters, sample_weight, init, n_init, max_iter, verbose, tol, random_state, copy_x, algorithm, oversampling_factor, init_iter, return_n_iter) and returns: est,  est,  est,  est, est,  est,  est