class_docstr: Softmax activation followed by a cross entropy loss.
class_docstr_tok: ["Softmax","activation","followed","by","a","cross","entropy","loss","."]
code: def backward(self, input_indexes, grad_outputs):
      func_grad = _SoftmaxCrossEntropyGrad_NoDoubleBackprop(
          self.ignore_label,
          self.class_weight,
          self.y,
          self._coeff,
          self.soft_target,
      )
      inputs = self.get_retained_inputs()
      return func_grad.apply(inputs + grad_outputs) + (None,)
  
imports: import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.functions.activation import log_softmax
  from chainer.utils import type_check
  from chainer import variable
  import chainerx
label_desc: Use image node_id 8 for calling the SoftmaxCrossEntropy obj's underlying member method code with example usage: obj.backward(input_indexes, grad_outputs) and returns: unknown