docstr: Args:
      box_cls (Tensor): tensor of shape (batch_size, num_proposals, K).
          The tensor predicts the classification probability for
          each proposal.
      box_pred (Tensor): tensors of shape (batch_size, num_proposals, 4).
          The tensor predicts 4-vector (x,y,w,h) box
          regression values for every proposal
  
  Returns:
      results (List[Instances]): a list of #images elements.
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code: def inference(self, box_cls, box_pred, cfg, device):
      """
      Args:
          box_cls (Tensor): tensor of shape (batch_size, num_proposals, K).
              The tensor predicts the classification probability for
              each proposal.
          box_pred (Tensor): tensors of shape (batch_size, num_proposals, 4).
              The tensor predicts 4-vector (x,y,w,h) box
              regression values for every proposal
  
      Returns:
          results (List[Instances]): a list of #images elements.
      """
      results = []
  
      if self.use_focal_loss or self.use_fed_loss:
          scores = torch.sigmoid(box_cls)
          labels = (
              torch.arange(self.num_classes, device=device)
              .unsqueeze(0)
              .repeat(self.num_proposals, 1)
              .flatten(0, 1)
          )
          box_pred_list = []
          scores_list = []
          labels_list = []
          for i, (scores_per_image, box_pred_per_image) in enumerate(
              zip(scores, box_pred)
          ):
              scores_per_image, topk_indices = scores_per_image.flatten(
                  0, 1
              ).topk(self.num_proposals, sorted=False)
              labels_per_image = labels[topk_indices]
              box_pred_per_image = (
                  box_pred_per_image.view(-1, 1, 4)
                  .repeat(1, self.num_classes, 1)
                  .view(-1, 4)
              )
              box_pred_per_image = box_pred_per_image[topk_indices]
  
              if self.use_ensemble and self.sampling_timesteps > 1:
                  box_pred_list.append(box_pred_per_image)
                  scores_list.append(scores_per_image)
                  labels_list.append(labels_per_image)
                  continue
  
              if self.use_nms:
                  det_bboxes, keep_idxs = batched_nms(
                      box_pred_per_image,
                      scores_per_image,
                      labels_per_image,
                      cfg.nms,
                  )
                  box_pred_per_image = box_pred_per_image[keep_idxs]
                  labels_per_image = labels_per_image[keep_idxs]
                  # some nms would reweight the score, such as softnms
                  scores_per_image = det_bboxes[:, -1]
              result = InstanceData()
              result.bboxes = box_pred_per_image
              result.scores = scores_per_image
              result.labels = labels_per_image
              results.append(result)
  
      else:
          # For each box we assign the best class or the second
          # best if the best on is `no_object`.
          scores, labels = F.softmax(box_cls, dim=-1)[:, :, :-1].max(-1)
  
          for i, (
              scores_per_image,
              labels_per_image,
              box_pred_per_image,
          ) in enumerate(zip(scores, labels, box_pred)):
              if self.use_ensemble and self.sampling_timesteps > 1:
                  return (
                      box_pred_per_image,
                      scores_per_image,
                      labels_per_image,
                  )
  
              if self.use_nms:
                  det_bboxes, keep_idxs = batched_nms(
                      box_pred_per_image,
                      scores_per_image,
                      labels_per_image,
                      cfg.nms,
                  )
                  box_pred_per_image = box_pred_per_image[keep_idxs]
                  labels_per_image = labels_per_image[keep_idxs]
                  # some nms would reweight the score, such as softnms
                  scores_per_image = det_bboxes[:, -1]
  
              result = InstanceData()
              result.bboxes = box_pred_per_image
              result.scores = scores_per_image
              result.labels = labels_per_image
              results.append(result)
      if self.use_ensemble and self.sampling_timesteps > 1:
          return box_pred_list, scores_list, labels_list
      else:
          return results
  
imports: import copy
  import math
  import random
  import warnings
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import build_activation_layer
  from mmcv.ops import batched_nms
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox2roi, bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, get_box_wh, scale_boxes
  from mmdet.utils import InstanceList
label_desc: Use image node_id 14 for calling the DynamicDiffusionDetHead obj's underlying member method code with example usage: obj.inference(box_cls, box_pred, cfg, device) and returns: box_pred_list,  scores_list,  labels_list, results, box_pred_per_image,  scores_per_image,  labels_per_image