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__()
  
          self.act_0 = nn.SiLU()
  
      def forward(self, x, y, z):
          x = self.act_0(x)
          y = self.act_0(y)
          z = self.act_0(z)
          return x, y, z
  
  def test():
      net = Model()
      net.eval()
  
      torch.manual_seed(0)
      x = torch.rand(12)
      y = torch.rand(12, 64)
      z = torch.rand(12, 24, 64)
  
      a = net(x, y, z)
  
      # export torchscript
      mod = torch.jit.trace(net, (x, y, z))
      mod.save("test_nn_SiLU.pt")
  
      # torchscript to pnnx
      import os
      os.system("../../src/pnnx test_nn_SiLU.pt inputshape=[12],[12,64],[12,24,64]")
  
      # ncnn inference
      import test_nn_SiLU_ncnn
      b = test_nn_SiLU_ncnn.test_inference()
  
      for a0, b0 in zip(a, b):
          if not torch.allclose(a0, b0, 1e-4, 1e-4):
              return False
      return True
  
  if __name__ == "__main__":
      if test():
          exit(0)
      else:
          exit(1)
  