class_docstr: Decode classification results.
  
  This decoder must work with `MultiClassEncoder` to reconstruct valid labels.
  The decoder expect results are after logits, e.g. Softmax.
  
  Parameters
  ----------
  axis : int
      Axis of class-wise results.
  thresh : float
      Confidence threshold for the post-softmax scores.
      Scores less than `thresh` are marked with `0`, corresponding `cls_id` is
      marked with invalid class id `-1`.
class_docstr_tok: ["Decode","classification","results",".","This","decoder","must","work","with","`","MultiClassEncoder","`","to","reconstruct","valid","labels",".","The","decoder","expect","results","are","after","logits",",","e.g",".","Softmax",".","Parameters","--","--","--","--","--","axis",":","int","Axis","of","class-wise","results",".","thresh",":","float","Confidence","threshold","for","the","post-softmax","scores",".","Scores","less","than","`","thresh","`","are","marked","with","`","0","`",",","corresponding","`","cls_id","`","is","marked","with","invalid","class","id","`","-1","`","."]
code: def __init__(self, axis=-1, thresh=0.01):
      super(MultiClassDecoder, self).__init__()
      self._axis = axis
      self._thresh = thresh
  
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 MultiClassDecoder object from inherited base classes: gluon with example: obj = MultiClassDecoder(axis, thresh)