code: def quadratic_loss(covariance, precision):
      """Computes ...
  
      Parameters
      ----------
      covariance : 2D ndarray (n_features, n_features)
          Maximum Likelihood Estimator of covariance
  
      precision : 2D ndarray (n_features, n_features)
          The precision matrix of the model to be tested
  
      Returns
      -------
      Quadratic loss
      """
      assert covariance.shape == precision.shape
      dim, _ = precision.shape
      return np.trace(
          (np.dot(covariance, precision) - np.eye(dim)) ** 2
      )
  
imports: import numpy
  from sklearn.utils.extmath import fast_logdet
label_desc: Use image node_id 3 for calling a global function with example usage: quadratic_loss(covariance, precision) and returns: np