Slice the correct dimensions for use in the kernel, as indicated by
        `self.active_dims` for covariance matrices. This requires slicing the
        rows *and* columns. This will also turn flattened diagonal
        matrices into a tensor of full diagonal matrices.
        :param cov: Tensor of covariance matrices (NxDxD or NxD).
        :return: N x self.input_dim x self.input_dim.

def _slice_cov(self, cov):
        """
        Slice the correct dimensions for use in the kernel, as indicated by
        `self.active_dims` for covariance matrices. This requires slicing the
        rows *and* columns. This will also turn flattened diagonal
        matrices into a tensor of full diagonal matrices.
        :param cov: Tensor of covariance matrices (NxDxD or NxD).
        :return: N x self.input_dim x self.input_dim.
        """
        cov = tf.cond(tf.equal(tf.rank(cov), 2), lambda: tf.matrix_diag(cov), lambda: cov)

        if isinstance(self.active_dims, slice):
            cov = cov[..., self.active_dims, self.active_dims]
        else:
            cov_shape = tf.shape(cov)
            covr = tf.reshape(cov, [-1, cov_shape[-1], cov_shape[-1]])
            gather1 = tf.gather(tf.transpose(covr, [2, 1, 0]), self.active_dims)
            gather2 = tf.gather(tf.transpose(gather1, [1, 0, 2]), self.active_dims)
            cov = tf.reshape(tf.transpose(gather2, [2, 0, 1]),
                             tf.concat([cov_shape[:-2], [len(self.active_dims), len(self.active_dims)]], 0))
        return cov