class_docstr: A pixel-wise classifier.
  
  It computes the loss based on a given input/label pair for
  semantic segmentation.
  
  Args:
      predictor (~chainer.Link): Predictor network.
      ignore_label (int): A class id that is going to be ignored in
          evaluation. The default value is -1.
      class_weight (array): An array
          that contains constant weights that will be multiplied with the
          loss values along with the channel dimension. This will be
          used in :func:`chainer.functions.softmax_cross_entropy`.
class_docstr_tok: ["A","pixel-wise","classifier",".","It","computes","the","loss","based","on","a","given","input\/label","pair","for","semantic","segmentation",".","Args",":","predictor","(","~chainer.Link",")",":","Predictor","network",".","ignore_label","(","int",")",":","A","class","id","that","is","going","to","be","ignored","in","evaluation",".","The","default","value","is","-1.","class_weight","(","array",")",":","An","array","that","contains","constant","weights","that","will","be","multiplied","with","the","loss","values","along","with","the","channel","dimension",".","This","will","be","used","in",":","func",":","`","chainer.functions.softmax_cross_entropy","`","."]
code: def to_cpu(self):
      super(PixelwiseSoftmaxClassifier, self).to_cpu()
      if self.class_weight is not None:
          self.class_weight = cuda.to_cpu(self.class_weight)
  
file_path: chainercv/chainercv/links/model/pixelwise_softmax_classifier.py
imports: import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer import reporter
  import numpy
label_desc: Use image node_id 2 for calling the PixelwiseSoftmaxClassifier obj's underlying member method code with example usage: obj.to_cpu() without return types