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 deserialize(inputs, name, state):
      return ProposalTargetLayer(
          inputs[0],
          inputs[1],
          batch_size=state["batch_size"],
          fg_fraction=state["fg_fraction"],
          normalize_targets=state["normalize_targets"],
          normalize_means=state["normalize_means"],
          normalize_stds=state["normalize_stds"],
          fg_thresh=state["fg_thresh"],
          bg_thresh_hi=state["bg_thresh_hi"],
          bg_thresh_lo=state["bg_thresh_lo"],
          param_str=state["param_str"],
          name=name,
      )
  
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 7 for calling the ProposalTargetLayer obj's underlying member method code with example usage: obj.deserialize(inputs, name, state) and returns: ProposalTargetLayer