docstr: Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`.
  
  Args:
      deltas (Tensor): transformation deltas of shape (N, k*4),
          where k >= 1. deltas[i] represents k potentially
          different class-specific box transformations for
          the single box boxes[i].
      boxes (Tensor): boxes to transform, of shape (N, 4)
docstr_tok: ["Apply","transformation","`","deltas","`","(","dx",",","dy",",","dw",",","dh",")","to","`","boxes","`",".","Args",":","deltas","(","Tensor",")",":","transformation","deltas","of","shape","(","N",",","k","*","4",")",",","where","k",">","=","1.","deltas","[","i","]","represents","k","potentially","different","class-specific","box","transformations","for","the","single","box","boxes","[","i","]",".","boxes","(","Tensor",")",":","boxes","to","transform",",","of","shape","(","N",",","4",")"]
code: def apply_deltas(self, deltas, boxes):
      """Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`.
  
      Args:
          deltas (Tensor): transformation deltas of shape (N, k*4),
              where k >= 1. deltas[i] represents k potentially
              different class-specific box transformations for
              the single box boxes[i].
          boxes (Tensor): boxes to transform, of shape (N, 4)
      """
      boxes = boxes.to(deltas.dtype)
  
      widths = boxes[:, 2] - boxes[:, 0]
      heights = boxes[:, 3] - boxes[:, 1]
      ctr_x = boxes[:, 0] + 0.5 * widths
      ctr_y = boxes[:, 1] + 0.5 * heights
  
      wx, wy, ww, wh = self.bbox_weights
      dx = deltas[:, 0::4] / wx
      dy = deltas[:, 1::4] / wy
      dw = deltas[:, 2::4] / ww
      dh = deltas[:, 3::4] / wh
  
      # Prevent sending too large values into torch.exp()
      dw = torch.clamp(dw, max=self.scale_clamp)
      dh = torch.clamp(dh, max=self.scale_clamp)
  
      pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
      pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
      pred_w = torch.exp(dw) * widths[:, None]
      pred_h = torch.exp(dh) * heights[:, None]
  
      pred_boxes = torch.zeros_like(deltas)
      pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w  # x1
      pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h  # y1
      pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w  # x2
      pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h  # y2
  
      return pred_boxes
  
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 3 for calling the SingleDiffusionDetHead obj's underlying member method code with example usage: obj.apply_deltas(deltas, boxes) and returns: pred_boxes