class_docstr: YOLOV3 target generator that requires network predictions.
  `Dynamic` indicate that the targets generated depend on current network.
  `Simple` indicate that it only support `pos_iou_thresh` >= 1.0,
  otherwise it's a lot more complicated and slower.
  (box regression targets and class targets are not necessary when `pos_iou_thresh` >= 1.0)
  
  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.
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code: def __init__(self, num_class, ignore_iou_thresh, **kwargs):
      super(YOLOV3DynamicTargetGeneratorSimple, self).__init__(**kwargs)
      self._num_class = num_class
      self._ignore_iou_thresh = ignore_iou_thresh
      self._batch_iou = BBoxBatchIOU()
  
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 YOLOV3DynamicTargetGeneratorSimple object from inherited base classes: gluon with example: obj = YOLOV3DynamicTargetGeneratorSimple(num_class, ignore_iou_thresh)