class_docstr: YOLO V3 prefetch target generator.
  The target generated by this instance is invariant to network predictions.
  Therefore it is usually used in DataLoader transform function to reduce the load on GPUs.
  
  Parameters
  ----------
  num_class : int
      Number of foreground classes.
class_docstr_tok: ["YOLO","V3","prefetch","target","generator",".","The","target","generated","by","this","instance","is","invariant","to","network","predictions",".","Therefore","it","is","usually","used","in","DataLoader","transform","function","to","reduce","the","load","on","GPUs",".","Parameters","--","--","--","--","--","num_class",":","int","Number","of","foreground","classes","."]
code: def __init__(self, num_class, **kwargs):
      super(YOLOV3PrefetchTargetGenerator, self).__init__(**kwargs)
      self._num_class = num_class
      self.bbox2center = BBoxCornerToCenter(axis=-1, split=True)
      self.bbox2corner = BBoxCenterToCorner(axis=-1, split=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 YOLOV3PrefetchTargetGenerator object from inherited base classes: gluon with example: obj = YOLOV3PrefetchTargetGenerator(num_class)