class_docstr: Base class for KMeans and MiniBatchKMeans
class_docstr_tok: ["Base","class","for","KMeans","and","MiniBatchKMeans"]
code: def _more_tags(self):
      return {
          "_xfail_checks": {
              "check_sample_weights_invariance": (
                  "zero sample_weight is not equivalent to removing samples"
              ),
          },
      }
  
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 14 for calling the _BaseKMeans obj's underlying member method code with example usage: obj._more_tags() and returns: dict