class_docstr: Softmax activation followed by a cross entropy loss.
class_docstr_tok: ["Softmax","activation","followed","by","a","cross","entropy","loss","."]
code: def __init__(
      self,
      normalize=True,
      cache_score=True,
      class_weight=None,
      ignore_label=-1,
      reduce="mean",
      soft_target_loss="cross-entropy",
  ):
      self.normalize = normalize
      self.cache_score = cache_score
      _check_class_weight_option(class_weight)
      self.class_weight = class_weight
      self.ignore_label = ignore_label
      _check_reduce_option(reduce)
      self.reduce = reduce
      self.soft_target_loss = soft_target_loss
  
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 1 to create a new SoftmaxCrossEntropy object from inherited base classes: function_node with example: obj = SoftmaxCrossEntropy(normalize, cache_score, class_weight, ignore_label, reduce, soft_target_loss)