class_docstr: Encode classification training target given matching results.
  
  This encoder will assign training target of matched bounding boxes to
  ground-truth label + 1 and negative samples with label 0.
  Ignored samples will be assigned with `ignore_label`, whose default is -1.
  
  Parameters
  ----------
  ignore_label : float
      Assigned to un-matched samples, they are neither positive or negative during
      training, and should be excluded in loss function. Default is -1.
class_docstr_tok: ["Encode","classification","training","target","given","matching","results",".","This","encoder","will","assign","training","target","of","matched","bounding","boxes","to","ground-truth","label","+","1","and","negative","samples","with","label","0",".","Ignored","samples","will","be","assigned","with","`","ignore_label","`",",","whose","default","is","-1",".","Parameters","--","--","--","--","--","ignore_label",":","float","Assigned","to","un-matched","samples",",","they","are","neither","positive","or","negative","during","training",",","and","should","be","excluded","in","loss","function",".","Default","is","-1","."]
code: def __init__(self, ignore_label=-1):
      super(MultiClassEncoder, self).__init__()
      self._ignore_label = ignore_label
  
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 MultiClassEncoder object from inherited base classes: gluon with example: obj = MultiClassEncoder(ignore_label)