class_docstr: If the detection is normalized, for example, ssd detected bounding box is in [0, 1],
  need to scale the bbox according to the original image size.
  Note that in this transformer, the tensor from model output will be decoded,
  just like `DecodeOutput`
code: def __init__(self, bigdl_type="float"):
      super(ScaleDetection, self).__init__(bigdl_type)
  
imports: import sys
  from bigdl.util.common import JavaValue
  from zoo.models.image.common.image_model import ImageModel
  from zoo.common.utils import callZooFunc
  from zoo.feature.image.imageset import *
  from zoo.feature.image.imagePreprocessing import *
label_desc: Use image node_id 1 to create a new ScaleDetection object from inherited base classes: ImagePreprocessing with example: obj = ScaleDetection(bigdl_type)