def test_view_of_view_modification():
    adata = ad.AnnData(np.zeros((10, 10)))
    adata[0, :][:, 5:].X = np.ones(5)
    assert np.all(adata.X[0, 5:] == np.ones(5))
    adata[[1, 2], :][:, [1, 2]].X = np.ones((2, 2))
    assert np.all(adata.X[1:3, 1:3] == np.ones((2, 2)))

    adata.X = sparse.csr_matrix(adata.X)
    adata[0, :][:, 5:].X = np.ones(5) * 2
    assert np.all(asarray(adata.X)[0, 5:] == np.ones(5) * 2)
    adata[[1, 2], :][:, [1, 2]].X = np.ones((2, 2)) * 2
    assert np.all(asarray(adata.X)[1:3, 1:3] == np.ones((2, 2)) * 2)
def cumsum(X):
        """
        Y[i] = sum(X[:i])
        """
        (m,) = X.shape
        s_state = te.placeholder((m + 1,), dtype="int32", name="state")
        s_init = te.compute((1,), lambda _: tvm.tir.const(0, "int32"))
        s_update = te.compute((m + 1,), lambda l: s_state[l - 1] + X[l - 1])
        return tvm.te.scan(s_init, s_update, s_state, inputs=[X], name="cumsum")