class_docstr: Base class for KMeans and MiniBatchKMeans
class_docstr_tok: ["Base","class","for","KMeans","and","MiniBatchKMeans"]
docstr: Issue an estimator specific warning when vcomp and mkl are both present
  
  This method is called by `_check_mkl_vcomp`.
docstr_tok: ["Issue","an","estimator","specific","warning","when","vcomp","and","mkl","are","both","present","This","method","is","called","by","`","_check_mkl_vcomp","`","."]
code: def _warn_mkl_vcomp(self, n_active_threads):
      """Issue an estimator specific warning when vcomp and mkl are both present
  
      This method is called by `_check_mkl_vcomp`.
      """
  
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 3 for calling the _BaseKMeans obj's underlying member method code with example usage: obj._warn_mkl_vcomp(n_active_threads) without return types