text: # Tencent is pleased to support the open source community by making ncnn available.
  #
  # Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
  #
  # Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  # in compliance with the License. You may obtain a copy of the License at
  #
  # https://opensource.org/licenses/BSD-3-Clause
  #
  # 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 torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  class Model(nn.Module):
      def __init__(self):
          super(Model, self).__init__()
  
      def forward(self, x, y, z, w):
          x = F.softshrink(x)
          y = F.softshrink(y, 0.1)
          z = F.softshrink(z, 0.22)
          w = F.softshrink(w, 0)
          return x, y, z, w
  
  def test():
      net = Model()
      net.eval()
  
      torch.manual_seed(0)
      x = torch.rand(1, 16)
      y = torch.rand(12, 2, 16)
      z = torch.rand(1, 3, 12, 16)
      w = torch.rand(1, 5, 7, 9, 11)
  
      a0, a1, a2, a3 = net(x, y, z, w)
  
      # export torchscript
      mod = torch.jit.trace(net, (x, y, z, w))
      mod.save("test_F_softshrink.pt")
  
      # torchscript to pnnx
      import os
      os.system("../src/pnnx test_F_softshrink.pt inputshape=[1,16],[12,2,16],[1,3,12,16],[1,5,7,9,11]")
  
      # pnnx inference
      import test_F_softshrink_pnnx
      b0, b1, b2, b3 = test_F_softshrink_pnnx.test_inference()
  
      return torch.equal(a0, b0) and torch.equal(a1, b1) and torch.equal(a2, b2) and torch.equal(a3, b3)
  
  if __name__ == "__main__":
      if test():
          exit(0)
      else:
          exit(1)
  