code: def bbox_transform(self, deltas, weights=[0.1, 0.1, 0.2, 0.2]):
      wx, wy, ww, wh = weights
  
      deltas = paddle.reshape(deltas, shape=(0, -1, 4))
  
      dx = paddle.slice(deltas, axes=[2], starts=[0], ends=[1]) * wx
      dy = paddle.slice(deltas, axes=[2], starts=[1], ends=[2]) * wy
      dw = paddle.slice(deltas, axes=[2], starts=[2], ends=[3]) * ww
      dh = paddle.slice(deltas, axes=[2], starts=[3], ends=[4]) * wh
  
      dw = paddle.clip(dw, -1.0e10, np.log(1000.0 / 16))
      dh = paddle.clip(dh, -1.0e10, np.log(1000.0 / 16))
  
      pred_ctr_x = dx
      pred_ctr_y = dy
      pred_w = paddle.exp(dw)
      pred_h = paddle.exp(dh)
  
      x1 = pred_ctr_x - 0.5 * pred_w
      y1 = pred_ctr_y - 0.5 * pred_h
      x2 = pred_ctr_x + 0.5 * pred_w
      y2 = pred_ctr_y + 0.5 * pred_h
  
      x1 = paddle.reshape(x1, shape=(-1,))
      y1 = paddle.reshape(y1, shape=(-1,))
      x2 = paddle.reshape(x2, shape=(-1,))
      y2 = paddle.reshape(y2, shape=(-1,))
  
      return paddle.concat([x1, y1, x2, y2])
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Normal, XavierUniform, KaimingNormal
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register, create
  from .roi_extractor import RoIAlign
  from ..shape_spec import ShapeSpec
  from ..bbox_utils import bbox2delta
  from ..cls_utils import _get_class_default_kwargs
  from ppdet.modeling.layers import ConvNormLayer
label_desc: Use image node_id 6 for calling the BBoxHead obj's underlying member method code with example usage: obj.bbox_transform(deltas, weights) and returns: paddle