code: def distribute_covar_matrix_to_match_covariance_type(
      tied_cv, covariance_type, n_components
  ):
      """Create all the covariance matrices from a given template."""
      if covariance_type == "spherical":
          cv = np.tile(
              tied_cv.mean() * np.ones(tied_cv.shape[1]),
              (n_components, 1),
          )
      elif covariance_type == "tied":
          cv = tied_cv
      elif covariance_type == "diag":
          cv = np.tile(np.diag(tied_cv), (n_components, 1))
      elif covariance_type == "full":
          cv = np.tile(tied_cv, (n_components, 1, 1))
      else:
          raise ValueError(
              "covariance_type must be one of "
              + "'spherical', 'tied', 'diag', 'full'"
          )
      return cv
  
imports: import numpy
  from scipy import linalg
  from time import time
  from ..base import BaseEstimator
  from ..utils import check_random_state, check_array, deprecated
  from ..utils.extmath import logsumexp
  from ..utils.validation import check_is_fitted
  from ..None import cluster
  from sklearn.externals.six.moves import zip
label_desc: Use image node_id 8 for calling a global function with example usage: distribute_covar_matrix_to_match_covariance_type(tied_cv, covariance_type, n_components) and returns: cv