def METHOD_NAME():
    node_type = 'paper'
    edge_type = ('paper', 'to', 'paper')
    node_id_dict = {node_type: torch.randperm(6)}
    x_dict = {node_type: torch.randn(6, 32)}
    y_dict = {node_type: torch.randint(0, 2, (6, ))}
    edge_id_dict = {edge_type: torch.randperm(12)}
    edge_attr_dict = {edge_type: torch.randn(12, 16)}
    store = LocalFeatureStore.from_hetero_data(
        node_id_dict,
        x_dict,
        y_dict,
        edge_id_dict,
        edge_attr_dict,
    )
    assert len(store.get_all_tensor_attrs()) == 3
    attrs = store.get_all_tensor_attrs()
    assert attrs[0].group_name == node_type
    assert attrs[0].attr_name == 'x'
    assert attrs[1].group_name == node_type
    assert attrs[1].attr_name == 'y'
    assert attrs[2].group_name == edge_type
    assert attrs[2].attr_name == 'edge_attr'
    assert torch.equal(
        store.get_global_id(group_name=node_type),
        node_id_dict[node_type],
    )
    assert torch.equal(
        store.get_tensor(group_name=node_type, attr_name='x'),
        x_dict[node_type],
    )
    assert torch.equal(
        store.get_tensor(group_name=node_type, attr_name='y'),
        y_dict[node_type],
    )
    assert torch.equal(
        store.get_global_id(group_name=edge_type),
        edge_id_dict[edge_type],
    )
    assert torch.equal(
        store.get_tensor(group_name=edge_type, attr_name='edge_attr'),
        edge_attr_dict[edge_type],
    )