code: def _to_cov(x, n):
      
  
      if np.isscalar(x):
          if x < 0:
              raise ValueError('covariance must be > 0')
          return np.eye(n) * x
  
      x = np.atleast_2d(x)
      try:
          # quickly find out if we are positive definite
          np.linalg.cholesky(x)
      except:
          raise ValueError('covariance must be positive definit')
  
      return x
docs: If x is a scalar, returns a covariance matrix generated from it
      as the identity matrix multiplied by x. The dimension will be nxn.
      If x is already a 2D numpy array then it is returned unchanged.
  
      Raises ValueError if not positive definite