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 warnings
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: distribute_covar_matrix_to_match_covariance_type(tied_cv, covariance_type, n_components) and returns: cv