def test_set_subset_obsm(adata, subset_func):
    init_hash = joblib.hash(adata)
    orig_obsm_val = adata.obsm["o"].copy()

    while True:
        subset_idx = slice_subset(adata.obs_names)
        if len(adata[subset_idx, :]) > 2:
            break
    subset = adata[subset_idx, :]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.obsm["o"].shape[0])), subset.obs_names
    )

    assert subset.is_view
    subset.obsm["o"][internal_idx] = 1
    assert not subset.is_view
    assert np.all(adata.obsm["o"] == orig_obsm_val)

    assert init_hash == joblib.hash(adata)
def _sparse_rmsprop_update_numpy(self, var, gindexs, gvalues, mg, rms, mom,
                                   lr, decay, momentum, centered):
    mg_t = copy.deepcopy(mg)
    rms_t = copy.deepcopy(rms)
    mom_t = copy.deepcopy(mom)
    var_t = copy.deepcopy(var)
    for i in range(len(gindexs)):
      gindex = gindexs[i]
      gvalue = gvalues[i]
      rms_t[gindex] = rms[gindex] * decay + (1 - decay) * gvalue * gvalue
      denom_t = rms_t[gindex]
      if centered:
        mg_t[gindex] = mg_t[gindex] * decay + (1 - decay) * gvalue
        denom_t -= mg_t[gindex] * mg_t[gindex]
      mom_t[gindex] = momentum * mom[gindex] + lr * gvalue / np.sqrt(denom_t)
      var_t[gindex] = var[gindex] - mom_t[gindex]
    return var_t, mg_t, rms_t, mom_t