def METHOD_NAME(self):
    x = self.create_data_double_batch()
    kernel = self.create_kernel(num_dims=x.size(-1), batch_shape=torch.Size([3, 2]))
    res1 = kernel(x).to_dense()[0, 1]  # Result of first kernel on first batch of data
    new_kernel = kernel[0, 1]
    res2 = new_kernel(x[0, 1]).to_dense()  # Should also be result of first kernel on first batch of data.
    self.assertLess(torch.norm(res1 - res2) / res1.norm(), 1e-4)
    # Test diagonal
    kernel_diag = kernel(x, diag=True)
    actual_diag = res1.diagonal(dim1=-1, dim2=-2)
    self.assertLess(torch.norm(kernel_diag - actual_diag) / actual_diag.norm(), 1e-4)