\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$

    In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow

    $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$

    Args:
        inputs (`torch.Tensor`):
            A tensor representing a mask.
        labels (`torch.Tensor`):
            A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
            (0 for the negative class and 1 for the positive class).
        num_masks (`int`):
            The number of masks present in the current batch, used for normalization.

    Returns:
        `torch.Tensor`: The computed loss.
    r