class_docstr: RCNN target encoder to generate matching target and regression target values.
  
  Parameters
  ----------
  num_class : int
      Number of total number of positive classes.
  max_pos : int, default is 128
      Upper bound of Number of positive samples.
  per_device_batch_size : int, default is 1
      Per device batch size
  means : iterable of float, default is (0., 0., 0., 0.)
      Mean values to be subtracted from regression targets.
  stds : iterable of float, default is (.1, .1, .2, .2)
      Standard deviations to be divided from regression targets.
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code: def __init__(
      self,
      num_class,
      max_pos=128,
      per_device_batch_size=1,
      means=(0.0, 0.0, 0.0, 0.0),
      stds=(0.1, 0.1, 0.2, 0.2),
  ):
      super(RCNNTargetGenerator, self).__init__()
      self._cls_encoder = MultiClassEncoder()
      self._box_encoder = NormalizedPerClassBoxCenterEncoder(
          num_class=num_class,
          max_pos=max_pos,
          per_device_batch_size=per_device_batch_size,
          means=means,
          stds=stds,
      )
  
file_path: gluon-cv/gluoncv/model_zoo/rcnn/faster_rcnn/rcnn_target.py
imports: from __future__ import absolute_import
  from mxnet import autograd
  from mxnet import gluon
  from ....nn.coder import MultiClassEncoder, NormalizedPerClassBoxCenterEncoder
label_desc: Use image node_id 1 to create a new RCNNTargetGenerator object from inherited base classes: gluon with example: obj = RCNNTargetGenerator(num_class, max_pos, per_device_batch_size, means, stds)