class_docstr: YOLOV3 target merger that merges the prefetched targets and dynamic targets.
  
  Parameters
  ----------
  num_class : int
      Number of foreground classes.
  ignore_iou_thresh : float
      Anchors that has IOU in `range(ignore_iou_thresh, pos_iou_thresh)` don't get
      penalized of objectness score.
class_docstr_tok: ["YOLOV3","target","merger","that","merges","the","prefetched","targets","and","dynamic","targets",".","Parameters","--","--","--","--","--","num_class",":","int","Number","of","foreground","classes",".","ignore_iou_thresh",":","float","Anchors","that","has","IOU","in","`","range","(","ignore_iou_thresh",",","pos_iou_thresh",")","`","do","n't","get","penalized","of","objectness","score","."]
code: def __init__(self, num_class, ignore_iou_thresh, **kwargs):
      super(YOLOV3TargetMerger, self).__init__(**kwargs)
      self._num_class = num_class
      self._dynamic_target = YOLOV3DynamicTargetGeneratorSimple(
          num_class, ignore_iou_thresh
      )
      self._label_smooth = False
  
file_path: gluon-cv/gluoncv/model_zoo/yolo/yolo_target.py
imports: from __future__ import absolute_import
  from __future__ import division
  import numpy
  from mxnet import gluon
  from mxnet import nd
  from mxnet import autograd
  from ...nn.bbox import BBoxCornerToCenter, BBoxCenterToCorner, BBoxBatchIOU
label_desc: Use image node_id 1 to create a new YOLOV3TargetMerger object from inherited base classes: gluon with example: obj = YOLOV3TargetMerger(num_class, ignore_iou_thresh)