text: # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
  
  # Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
  # to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
  # and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
  
  # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
  # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
  # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  
  
  import torch
  
  torch.manual_seed(0)
  torch.set_printoptions(precision=8)
  # Default
  x = torch.rand(2, 3, 4, 5, requires_grad=True)
  z = torch.std(x)
  z.backward()
  print("Input = ", x)
  print("Result (Default) = ", z)
  print("Gradient for input (Default) = ", x.grad)
  
  # By dimension
  dimensions = [0, 1, 2, 3]
  names = ["Batch", "Depth", "Height", "Width"]
  for dim in dimensions:
      x = torch.rand(2, 3, 4, 5, requires_grad=True)
      z = torch.std(x, dim)
      z.sum().backward()
      print("Result (" + names[dim] + ") = ", z)
      print("Gradient for input (" + names[dim] + ") = ", x.grad)
  