class_docstr: Encode bounding boxes training target with normalized center offsets.
  
  Input bounding boxes are using corner type: `x_{min}, y_{min}, x_{max}, y_{max}`.
  
  Parameters
  ----------
  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
  stds : array-like of size 4
      Std value to be divided from encoded values, default is (0.1, 0.1, 0.2, 0.2).
  means : array-like of size 4
      Mean value to be subtracted from encoded values, default is (0., 0., 0., 0.).
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code: def __init__(
      self,
      num_class,
      max_pos=128,
      per_device_batch_size=1,
      stds=(0.1, 0.1, 0.2, 0.2),
      means=(0.0, 0.0, 0.0, 0.0),
  ):
      super(NormalizedPerClassBoxCenterEncoder, self).__init__()
      assert len(stds) == 4, "Box Encoder requires 4 std values."
      assert num_class > 0, "Number of classes must be positive"
      self._num_class = num_class
      self._max_pos = max_pos
      self._batch_size = per_device_batch_size
      with self.name_scope():
          self.class_agnostic_encoder = NormalizedBoxCenterEncoder(
              stds=stds, means=means
          )
          if "box_encode" in nd.contrib.__dict__:
              self.means = self.params.get_constant("means", means)
              self.stds = self.params.get_constant("stds", stds)
  
imports: from __future__ import absolute_import
  import numpy
  from mxnet import gluon
  from mxnet import nd
  from .bbox import BBoxCornerToCenter, NumPyBBoxCornerToCenter
label_desc: Use image node_id 1 to create a new NormalizedPerClassBoxCenterEncoder object from inherited base classes: gluon with example: obj = NormalizedPerClassBoxCenterEncoder(num_class, max_pos, per_device_batch_size, stds, means)