class_docstr: Parameter for scaling the regression outputs.
class_docstr_tok: ["Parameter","for","scaling","the","regression","outputs","."]
code: def __init__(self, scale=1.0):
      super(ScaleReg, self).__init__()
      scale = paddle.to_tensor(scale)
      self.scale = self.create_parameter(
          shape=[1],
          dtype="float32",
          default_initializer=nn.initializer.Assign(scale),
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  from paddle.nn.initializer import Normal, Constant
  from ..bbox_utils import batch_distance2bbox
  from ..losses import GIoULoss
  from ..initializer import bias_init_with_prob, constant_, normal_
  from ..assigners.utils import generate_anchors_for_grid_cell
  from ppdet.modeling.backbones.cspresnet import ConvBNLayer, RepVggBlock
  from ppdet.modeling.ops import get_static_shape, get_act_fn
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 1 to create a new ScaleReg object from inherited base classes: nn with example: obj = ScaleReg(scale)