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 hybrid_forward(self, F, x):
      pos_x = x.slice_axis(axis=self._axis, begin=1, end=None)
      cls_id = F.argmax(pos_x, self._axis)
      scores = F.pick(pos_x, cls_id, axis=-1)
      mask = scores > self._thresh
      cls_id = F.where(mask, cls_id, F.ones_like(cls_id) * -1)
      scores = F.where(mask, scores, F.zeros_like(scores))
      return cls_id, scores
  
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 2 for calling the MultiClassDecoder obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: cls_id,  scores