def METHOD_NAME() -> Iterable[Dict[str, Optional[torch.Tensor]]]:
    return [
        # random_inputs
        {
            "labels": torch.tensor([[1, 0, 0, 1, 1]]),
            "predictions": torch.tensor([[0.2, 0.6, 0.8, 0.4, 0.9]]),
            "weights": torch.tensor([[0.1, 0.1, 0.1, 0.1, 0.6]]),
            "expected_weighted_avg": torch.tensor([0.74]),
        },
        # no weight
        {
            "labels": torch.tensor([[1, 0, 1, 0, 1, 0]]),
            "predictions": torch.tensor([[0.5] * 6]),
            "weights": None,
            "expected_weighted_avg": torch.tensor([0.5]),
        },
        # all weights are zero
        {
            "labels": torch.tensor([[1, 1, 1, 1, 1]]),
            "predictions": torch.tensor([[0.2, 0.6, 0.8, 0.4, 0.9]]),
            "weights": torch.tensor([[0] * 5]),
            "expected_weighted_avg": torch.tensor([float("nan")]),
        },
    ]