def test_view_delitem(attr):
    adata = gen_adata((10, 10))
    getattr(adata, attr)["to_delete"] = np.ones((10, 10))
    # Shouldn’t be a subclass, should be an ndarray
    assert type(getattr(adata, attr)["to_delete"]) is np.ndarray
    view = adata[5:7, :][:, :5]
    adata_hash = joblib.hash(adata)
    view_hash = joblib.hash(view)

    getattr(view, attr).__delitem__("to_delete")

    assert not view.is_view
    assert "to_delete" not in getattr(view, attr)
    assert "to_delete" in getattr(adata, attr)
    assert adata_hash == joblib.hash(adata)
    assert view_hash != joblib.hash(view)
def rpc_bulk(orchestrator, audit_name, rpc_code, *arguments):

    # Get the implementor for the RPC code.
    # Raise NotImplementedError if it's not defined.
    try:
        method, blocking = rpcMap[rpc_code]
    except KeyError:
        raise NotImplementedError("RPC code not implemented: %r" % rpc_code)

    # This can't be done with blocking implementors!
    if blocking:
        raise NotImplementedError(
            "Cannot run blocking RPC calls in bulk. Code: %r" % rpc_code)

    # Prepare a partial function call to the implementor.
    caller = partial(method, orchestrator, audit_name)

    # Use the built-in map() function to issue all the calls.
    # This ensures we support the exact same interface and functionality.
    return map(caller, *arguments)