code: def _check_means(means, n_components, n_features):
      """Validate the provided 'means'.
  
      Parameters
      ----------
      means : array-like, shape (n_components, n_features)
          The centers of the current components.
  
      n_components : int
          Number of components.
  
      n_features : int
          Number of features.
  
      Returns
      -------
      means : array, (n_components, n_features)
      """
      means = check_array(
          means, dtype=[np.float64, np.float32], ensure_2d=False
      )
      _check_shape(means, (n_components, n_features), "means")
      return means
  
imports: import numpy
  from scipy import linalg
  from .base import BaseMixture, _check_shape
  from ..externals.six.moves import zip
  from ..utils import check_array
  from ..utils.validation import check_is_fitted
  from ..utils.extmath import row_norms
label_desc: Use image node_id 2 for calling a global function with example usage: _check_means(means, n_components, n_features) and returns: means