old_contents: from .affine import Affine
  from ... import describe
  from ...describe import Dimension, Synapses, Biases
  from ...check import has_shape
  from ... import check
  
  
  @describe.attributes(
      W=Synapses("Weights matrix",
          lambda obj: (obj.nO, obj.nI),
          lambda W, ops: None)
  )
  class Softmax(Affine):
      name = 'softmax'
      @check.arg(1, has_shape(('nB', 'nI')))
      def predict(self, input__BI):
          output__BO = self.ops.affine(self.W, self.b, input__BI)
          output__BO = self.ops.softmax(output__BO, inplace=False)
          return output__BO
  
      @check.arg(1, has_shape(('nB', 'nI')))
      def begin_update(self, input__BI, drop=0.):
          output__BO = self.predict(input__BI)
          @check.arg(0, has_shape(('nB', 'nO')))
          def finish_update(grad__BO, sgd=None):
              self.d_W += self.ops.batch_outer(grad__BO, input__BI)
              self.d_b += grad__BO.sum(axis=0)
              grad__BI = self.ops.dot(grad__BO, self.W)
              if sgd is not None:
                  sgd(self._mem.weights, self._mem.gradient, key=self.id)
              return grad__BI
          return output__BO, finish_update
  
new_contents: from .affine import Affine
  from ... import describe
  from ...describe import Dimension, Synapses, Biases
  from ...check import has_shape
  from ... import check
  
  
  @describe.attributes(
      W=Synapses("Weights matrix",
          lambda obj: (obj.nO, obj.nI),
          lambda W, ops: None)
  )
  class Softmax(Affine):
      name = 'softmax'
      @check.arg(1, has_shape(('nB', 'nI')))
      def predict(self, input__BI):
          output__BO = self.ops.affine(self.W, self.b, input__BI)
          output__BO = self.ops.softmax(output__BO, inplace=False)
          return output__BO
  
      @check.arg(1, has_shape(('nB', 'nI')))
      def begin_update(self, input__BI, drop=0.):
          output__BO = self.predict(input__BI)
          @check.arg(0, has_shape(('nB', 'nO')))
          def finish_update(grad__BO, sgd=None):
              self.d_W += self.ops.gemm(grad__BO, input__BI, trans1=True)
              self.d_b += grad__BO.sum(axis=0)
              grad__BI = self.ops.gemm(grad__BO, self.W)
              if sgd is not None:
                  sgd(self._mem.weights, self._mem.gradient, key=self.id)
              return grad__BI
          return output__BO, finish_update
  
subject: Fix gemm calls in Softmax