code: def _covar_mstep_diag(
      gmm, X, responsibilities, weighted_X_sum, norm, min_covar
  ):
      """Perform the covariance M step for diagonal cases."""
      avg_X2 = np.dot(responsibilities.T, X * X) * norm
      avg_means2 = gmm.means_**2
      avg_X_means = gmm.means_ * weighted_X_sum * norm
      return avg_X2 - 2 * avg_X_means + avg_means2 + min_covar
  
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 9 for calling a global function with example usage: _covar_mstep_diag(gmm, X, responsibilities, weighted_X_sum, norm, min_covar) and returns: unknown