code: def _validate_center_shape(X, n_centers, centers):
      """Check if centers is compatible with X and n_centers"""
      if len(centers) != n_centers:
          raise ValueError(
              "The shape of the initial centers (%s) "
              "does not match the number of clusters %i"
              % (centers.shape, n_centers)
          )
      if centers.shape[1] != X.shape[1]:
          raise ValueError(
              "The number of features of the initial centers %s "
              "does not match the number of features of the data %s."
              % (centers.shape[1], X.shape[1])
          )
  
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 1 for calling a global function with example usage: _validate_center_shape(X, n_centers, centers) without return types