class_docstr: Assign object detection proposals to ground-truth targets. Produces proposal
  classification labels and bounding-box regression targets.
class_docstr_tok: ["Assign","object","detection","proposals","to","ground-truth","targets",".","Produces","proposal","classification","labels","and","bounding-box","regression","targets","."]
code: def clone(self, cloned_inputs):
      return ProposalTargetLayer(
          cloned_inputs[0],
          cloned_inputs[1],
          batch_size=self._batch_size,
          fg_fraction=self._fg_fraction,
          normalize_targets=self._normalize_targets,
          normalize_means=self._normalize_means,
          normalize_stds=self._normalize_stds,
          fg_thresh=self._fg_thresh,
          bg_thresh_hi=self._bg_thresh_hi,
          bg_thresh_lo=self._bg_thresh_lo,
          param_str=self._param_str,
      )
  
file_path: CNTK/Examples/Image/Detection/utils/rpn/proposal_target_layer.py
imports: from cntk import output_variable, FreeDimension
  from cntk.ops.functions import UserFunction
  import yaml
  import numpy
  import numpy.random
  from utils.rpn.bbox_transform import bbox_transform
  from utils.cython_modules.cython_bbox import bbox_overlaps
label_desc: Use image node_id 5 for calling the ProposalTargetLayer obj's underlying member method code with example usage: obj.clone(cloned_inputs) and returns: ProposalTargetLayer