def _compute_Kdiag(self, F, X, variance):
    '\n        The internal interface for the actual computation for the diagonal of the covariance matrix.\n\n        :param F: MXNet computation type <mx.sym, mx.nd>.\n        :param X: the first set of inputs to the kernel.\n        :type X: MXNet NDArray or MXNet Symbol\n        :param variance: the variance parameter.\n        :type variance: MXNet NDArray or MXNet Symbol\n        :return: The covariance matrix.\n        :rtype: MXNet NDArray or MXNet Symbol\n        '
    return broadcast_to_w_samples(F, variance, X.shape[:(- 1)])
The internal interface for the actual computation for the diagonal of the covariance matrix.

:param F: MXNet computation type <mx.sym, mx.nd>.
:param X: the first set of inputs to the kernel.
:type X: MXNet NDArray or MXNet Symbol
:param variance: the variance parameter.
:type variance: MXNet NDArray or MXNet Symbol
:return: The covariance matrix.
:rtype: MXNet NDArray or MXNet Symbol
def _compute_Kdiag(self, F, X, variance):
    '\n        The internal interface for the actual computation for the diagonal of the covariance matrix.\n\n        :param F: MXNet computation type <mx.sym, mx.nd>.\n        :param X: the first set of inputs to the kernel.\n        :type X: MXNet NDArray or MXNet Symbol\n        :param variance: the variance parameter.\n        :type variance: MXNet NDArray or MXNet Symbol\n        :return: The covariance matrix.\n        :rtype: MXNet NDArray or MXNet Symbol\n        '
    return broadcast_to_w_samples(F, variance, X.shape[:(- 1)])