class_docstr: Hinge loss.
class_docstr_tok: ["Hinge","loss","."]
code: def backward_cpu(self, inputs, grad_outputs):
      t, gloss = inputs[1], grad_outputs[0]
  
      if self.reduce == "mean":
          gloss /= len(t)
  
      self.bottom_diff[numpy.arange(len(t)), t] *= -1
      if self.norm == "L1":
          gx = gloss * numpy.sign(self.bottom_diff)
      elif self.norm == "L2":
          gx = 2 * gloss * self.bottom_diff
      else:
          raise NotImplementedError()
  
      return gx, None
  
file_path: chainer/chainer/functions/loss/hinge.py
imports: import numpy
  from chainer import backend
  from chainer.backends import cuda
  from chainer import function
  from chainer.utils import type_check
label_desc: Use image node_id 5 for calling the Hinge obj's underlying member method code with example usage: obj.backward_cpu(inputs, grad_outputs) and returns: gx,  None