class_docstr: Base class for KMeans and MiniBatchKMeans
class_docstr_tok: ["Base","class","for","KMeans","and","MiniBatchKMeans"]
docstr: Opposite of the value of X on the K-means objective.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      New data.
  
  y : Ignored
      Not used, present here for API consistency by convention.
  
  sample_weight : array-like of shape (n_samples,), default=None
      The weights for each observation in X. If None, all observations
      are assigned equal weight.
  
  Returns
  -------
  score : float
      Opposite of the value of X on the K-means objective.
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code: def score(self, X, y=None, sample_weight=None):
      """Opposite of the value of X on the K-means objective.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          New data.
  
      y : Ignored
          Not used, present here for API consistency by convention.
  
      sample_weight : array-like of shape (n_samples,), default=None
          The weights for each observation in X. If None, all observations
          are assigned equal weight.
  
      Returns
      -------
      score : float
          Opposite of the value of X on the K-means objective.
      """
      check_is_fitted(self)
  
      X = self._check_test_data(X)
      sample_weight = _check_sample_weight(
          sample_weight, X, dtype=X.dtype
      )
  
      _, scores = _labels_inertia_threadpool_limit(
          X, sample_weight, self.cluster_centers_, self._n_threads
      )
      return -scores
  
imports: import warnings
  from abc import ABC, abstractmethod
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, _fit_context
  from ..exceptions import ConvergenceWarning
  from ..metrics.pairwise import _euclidean_distances, euclidean_distances
  from ..utils import check_array, check_random_state
  from ..utils._openmp_helpers import _openmp_effective_n_threads
  from ..utils._param_validation import Interval, StrOptions, validate_params
  from ..utils.extmath import row_norms, stable_cumsum
  from ..utils.fixes import threadpool_info, threadpool_limits
  from ..utils.sparsefuncs import mean_variance_axis
  from ..utils.sparsefuncs_fast import assign_rows_csr
  from ..utils.validation import _check_sample_weight, _is_arraylike_not_scalar, check_is_fitted
  from ._k_means_common import CHUNK_SIZE, _inertia_dense, _inertia_sparse, _is_same_clustering
  from ._k_means_elkan import elkan_iter_chunked_dense, elkan_iter_chunked_sparse, init_bounds_dense, init_bounds_sparse
  from ._k_means_lloyd import lloyd_iter_chunked_dense, lloyd_iter_chunked_sparse
  from ._k_means_minibatch import _minibatch_update_dense, _minibatch_update_sparse
label_desc: Use image node_id 13 for calling the _BaseKMeans obj's underlying member method code with example usage: obj.score(X, y, sample_weight) and returns: unknown