def test_not_set_subset_X(matrix_type, subset_func):
    adata = ad.AnnData(matrix_type(asarray(sparse.random(20, 20))))
    init_hash = joblib.hash(adata)
    orig_X_val = adata.X.copy()
    while True:
        subset_idx = slice_subset(adata.obs_names)
        if len(adata[subset_idx, :]) > 2:
            break
    subset = adata[subset_idx, :]

    subset = adata[:, subset_idx]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.X.shape[1])), subset.var_names
    )
    assert subset.is_view
    subset.X[:, internal_idx] = 1
    assert not subset.is_view
    assert not np.any(asarray(adata.X != orig_X_val))

    assert init_hash == joblib.hash(adata)
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")