def forward(self, x, boxes):
    '\n        Arguments:\n            x (tuple[tensor, tensor]): x contains the class logits\n                and the box_regression from the model.\n            boxes (list[BoxList]): bounding boxes that are used as\n                reference, one for ech image\n\n        Returns:\n            results (list[BoxList]): one BoxList for each image, containing\n                the extra fields labels and scores\n        '
    (class_logits, box_regression) = x
    class_prob = F.softmax(class_logits, (- 1))
    image_shapes = [box.size for box in boxes]
    boxes_per_image = [len(box) for box in boxes]
    concat_boxes = torch.cat([a.bbox for a in boxes], dim=0)
    if self.cls_agnostic_bbox_reg:
        box_regression = box_regression[:, (- 4):]
    proposals = self.box_coder.decode(box_regression.view(sum(boxes_per_image), (- 1)), concat_boxes)
    if self.cls_agnostic_bbox_reg:
        proposals = proposals.repeat(1, class_prob.shape[1])
    num_classes = class_prob.shape[1]
    proposals = proposals.split(boxes_per_image, dim=0)
    class_prob = class_prob.split(boxes_per_image, dim=0)
    results = []
    for (prob, boxes_per_img, image_shape) in zip(class_prob, proposals, image_shapes):
        boxlist = self.prepare_boxlist(boxes_per_img, prob, image_shape)
        boxlist = boxlist.clip_to_image(remove_empty=False)
        if (not self.bbox_aug_enabled):
            boxlist = self.filter_results(boxlist, num_classes)
        results.append(boxlist)
    return results
Arguments:
    x (tuple[tensor, tensor]): x contains the class logits
        and the box_regression from the model.
    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 fields labels and scores
def forward(self, x, boxes):
    '\n        Arguments:\n            x (tuple[tensor, tensor]): x contains the class logits\n                and the box_regression from the model.\n            boxes (list[BoxList]): bounding boxes that are used as\n                reference, one for ech image\n\n        Returns:\n            results (list[BoxList]): one BoxList for each image, containing\n                the extra fields labels and scores\n        '
    (class_logits, box_regression) = x
    class_prob = F.softmax(class_logits, (- 1))
    image_shapes = [box.size for box in boxes]
    boxes_per_image = [len(box) for box in boxes]
    concat_boxes = torch.cat([a.bbox for a in boxes], dim=0)
    if self.cls_agnostic_bbox_reg:
        box_regression = box_regression[:, (- 4):]
    proposals = self.box_coder.decode(box_regression.view(sum(boxes_per_image), (- 1)), concat_boxes)
    if self.cls_agnostic_bbox_reg:
        proposals = proposals.repeat(1, class_prob.shape[1])
    num_classes = class_prob.shape[1]
    proposals = proposals.split(boxes_per_image, dim=0)
    class_prob = class_prob.split(boxes_per_image, dim=0)
    results = []
    for (prob, boxes_per_img, image_shape) in zip(class_prob, proposals, image_shapes):
        boxlist = self.prepare_boxlist(boxes_per_img, prob, image_shape)
        boxlist = boxlist.clip_to_image(remove_empty=False)
        if (not self.bbox_aug_enabled):
            boxlist = self.filter_results(boxlist, num_classes)
        results.append(boxlist)
    return results