def METHOD_NAME(self):
    active_dims = list(range(3, 9))
    x = self.create_data_no_batch()
    kernel = self.create_kernel(num_dims=6, active_dims=active_dims)
    y = torch.randn_like(x[..., 0])
    kernel.initialize_from_data(x, y)
    kernel.initialize_from_data_empspect(x, y)
    covar_mat = kernel(x).evaluate_kernel().to_dense()
    kernel_basic = self.create_kernel(num_dims=6)
    kernel_basic.raw_mixture_weights.data = kernel.raw_mixture_weights
    kernel_basic.raw_mixture_means.data = kernel.raw_mixture_means
    kernel_basic.raw_mixture_scales.data = kernel.raw_mixture_scales
    covar_mat_actual = kernel_basic(x[:, active_dims]).evaluate_kernel().to_dense()
    self.assertLess(torch.norm(covar_mat - covar_mat_actual) / covar_mat_actual.norm(), 1e-4)