class_docstr: Encode bounding boxes training target with normalized center offsets using numpy.
  
  Input bounding boxes are using corner type: `x_{min}, y_{min}, x_{max}, y_{max}`.
  
  Parameters
  ----------
  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.).
class_docstr_tok: ["Encode","bounding","boxes","training","target","with","normalized","center","offsets","using","numpy",".","Input","bounding","boxes","are","using","corner","type",":","`","x_","{","min","}",",","y_","{","min","}",",","x_","{","max","}",",","y_","{","max","}","`",".","Parameters","--","--","--","--","--","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",".",")","."]
code: def __init__(
      self, stds=(0.1, 0.1, 0.2, 0.2), means=(0.0, 0.0, 0.0, 0.0)
  ):
      super(NumPyNormalizedBoxCenterEncoder, self).__init__()
      assert len(stds) == 4, "Box Encoder requires 4 std values."
      self._stds = stds
      self._means = means
      self.corner_to_center = NumPyBBoxCornerToCenter(split=True)
  
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 NumPyNormalizedBoxCenterEncoder object from inherited base classes: object with example: obj = NumPyNormalizedBoxCenterEncoder(stds, means)