class_docstr: Decode bounding boxes training target with normalized center offsets.
  This decoder must cooperate with NormalizedBoxCenterEncoder of same `stds`
  in order to get properly reconstructed bounding boxes.
  
  Returned 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).
  clip : float, default is None
      If given, bounding box target will be clipped to this value.
  convert_anchor : boolean, default is False
      Whether to convert anchor from corner to center format.
  minimal_opset : bool
      We sometimes add special operators to accelerate training/inference, however, for exporting
      to third party compilers we want to utilize most widely used operators.
      If `minimal_opset` is `True`, the network will use a minimal set of operators good
      for e.g., `TVM`.
class_docstr_tok: ["Decode","bounding","boxes","training","target","with","normalized","center","offsets",".","This","decoder","must","cooperate","with","NormalizedBoxCenterEncoder","of","same","`","stds","`","in","order","to","get","properly","reconstructed","bounding","boxes",".","Returned","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",")",".","clip",":","float",",","default","is","None","If","given",",","bounding","box","target","will","be","clipped","to","this","value",".","convert_anchor",":","boolean",",","default","is","False","Whether","to","convert","anchor","from","corner","to","center","format",".","minimal_opset",":","bool","We","sometimes","add","special","operators","to","accelerate","training\/inference",",","however",",","for","exporting","to","third","party","compilers","we","want","to","utilize","most","widely","used","operators",".","If","`","minimal_opset","`","is","`","True","`",",","the","network","will","use","a","minimal","set","of","operators","good","for","e.g.",",","`","TVM","`","."]
code: def __init__(
      self,
      stds=(0.1, 0.1, 0.2, 0.2),
      convert_anchor=False,
      clip=None,
      minimal_opset=False,
  ):
      super(NormalizedBoxCenterDecoder, self).__init__()
      assert len(stds) == 4, "Box Encoder requires 4 std values."
      self._stds = stds
      self._clip = clip
      if convert_anchor:
          self.corner_to_center = BBoxCornerToCenter(split=True)
      else:
          self.corner_to_center = None
      self._format = "corner" if convert_anchor else "center"
      self._minimal_opset = minimal_opset
  
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 NormalizedBoxCenterDecoder object from inherited base classes: gluon with example: obj = NormalizedBoxCenterDecoder(stds, convert_anchor, clip, minimal_opset)