def __init__(
        self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0, num_points: int = 12544
    ):
        """This class computes an assignment between the labels and the predictions of the network.

        For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
        predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are
        un-matched (and thus treated as non-objects).

        Params:
            cost_class (float, *optional*, defaults to 1.0):
                This is the relative weight of the classification error in the matching cost.
            cost_mask (float, *optional*,  defaults to 1.0):
                This is the relative weight of the sigmoid ce loss of the binary mask in the matching cost.
            cost_dice (float, *optional*, defaults to 1.0):
                This is the relative weight of the dice loss of the binary mask in the matching cost
            num_points (int, *optional*, defaults to 12544):
                Number of points to be sampled for dice and mask loss matching cost.
        """
        super().__init__()
        if cost_class == 0 and cost_mask == 0 and cost_dice == 0:
            raise ValueError("All costs cant be 0")
        self.cost_class = cost_class
        self.cost_mask = cost_mask
        self.cost_dice = cost_dice
        self.num_points = num_points
This class computes an assignment between the labels and the predictions of the network.

        For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
        predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are
        un-matched (and thus treated as non-objects).

        Params:
            cost_class (float, *optional*, defaults to 1.0):
                This is the relative weight of the classification error in the matching cost.
            cost_mask (float, *optional*,  defaults to 1.0):
                This is the relative weight of the sigmoid ce loss of the binary mask in the matching cost.
            cost_dice (float, *optional*, defaults to 1.0):
                This is the relative weight of the dice loss of the binary mask in the matching cost
            num_points (int, *optional*, defaults to 12544):
                Number of points to be sampled for dice and mask loss matching cost.
        
dpkd/transformers/src/transformers/models/oneformer/modeling_oneformer.py