Arguments:
            x (Tensor): the mask logits
            boxes (list[BoxList]): bounding boxes that are used as
                reference, one for ech image

        Returns:
            results (list[BoxList]): one BoxList for each image, containing
                the extra field mask

def forward(self, x, boxes):
        """
        Arguments:
            x (Tensor): the mask logits
            boxes (list[BoxList]): bounding boxes that are used as
                reference, one for ech image

        Returns:
            results (list[BoxList]): one BoxList for each image, containing
                the extra field mask
        """
        mask_prob = x.sigmoid()

        # select masks coresponding to the predicted classes
        num_masks = x.shape[0]
        labels = [bbox.get_field("labels") for bbox in boxes]
        labels = torch.cat(labels)
        index = torch.arange(num_masks, device=labels.device)
        mask_prob = mask_prob[index, labels][:, None]

        boxes_per_image = [len(box) for box in boxes]
        mask_prob = mask_prob.split(boxes_per_image, dim=0)

        if self.masker:
            mask_prob = self.masker(mask_prob, boxes)

        results = []
        for prob, box in zip(mask_prob, boxes):
            bbox = BoxList(box.bbox, box.size, mode="xyxy")
            for field in box.fields():
                bbox.add_field(field, box.get_field(field))
            bbox.add_field("mask", prob)
            results.append(bbox)

        return results