text: # Copyright 2019,2020,2021 Sony Corporation.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  
  import nnabla.functions as F
  
  from .utils import no_grad, positive_axis
  
  
  def categorical_cross_entropy_backward(inputs, axis=None):
      """
      Args:
        inputs (list of nn.Variable): Incomming grads/inputs to/of the forward function.
        kwargs (dict of arguments): Dictionary of the corresponding function arguments.
  
      Return:
        list of Variable: Return the gradients wrt inputs of the corresponding function.
      """
      dy = inputs[0]
      x0 = inputs[1]
      t0 = inputs[2]
  
      D = len(x0.shape)
      axis = positive_axis(axis, D)
      c0 = x0.shape[axis]
      t0_shape = [s for s in t0.shape if s != 1]
      u0 = F.reshape(t0, (-1, 1), inplace=False)
      u1 = F.one_hot(u0, (c0, ))
      to = F.reshape(u1, t0_shape + [c0, ])
      t0 = no_grad(to)
      if axis != len(to.shape) - 1:
          oaxes = [i for i in range(len(t0_shape))]
          taxes = oaxes[:axis] + [to.ndim - 1] + oaxes[axis:]
          to = F.transpose(to, taxes)
      dx0 = -dy * to / x0
      return dx0, None
  