class_docstr: Assign anchors to ground-truth targets. Produces anchor classification
  labels and bounding-box regression targets.
class_docstr_tok: ["Assign","anchors","to","ground-truth","targets",".","Produces","anchor","classification","labels","and","bounding-box","regression","targets","."]
code: def deserialize(inputs, name, state):
      return AnchorTargetLayer(
          inputs[0],
          inputs[1],
          inputs[2],
          rpn_batch_size=state["rpn_batch_size"],
          rpn_fg_fraction=state["rpn_fg_fraction"],
          clobber_positives=state["clobber_positives"],
          positive_overlap=state["positive_overlap"],
          negative_overlap=state["negative_overlap"],
          param_str=state["param_str"],
          name=name,
      )
  
imports: import os
  from cntk import output_variable
  from cntk.ops.functions import UserFunction
  import yaml
  import numpy
  import numpy.random
  from utils.rpn.generate_anchors import generate_anchors
  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 AnchorTargetLayer obj's underlying member method code with example usage: obj.deserialize(inputs, name, state) and returns: AnchorTargetLayer