code: # 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):
          x = F.avg_pool1d(x, kernel_size=3)
          x = F.avg_pool1d(x, kernel_size=4, stride=2, padding=2)
          x = F.avg_pool1d(x, kernel_size=3, stride=1, padding=(0), ceil_mode=False, count_include_pad=True)
          x = F.avg_pool1d(x, kernel_size=5, stride=2, padding=(2), ceil_mode=True, count_include_pad=False)
          x = F.avg_pool1d(x, kernel_size=3, stride=2, padding=1, ceil_mode=False, count_include_pad=True)
          x = F.avg_pool1d(x, kernel_size=2, stride=1, padding=0, ceil_mode=True, count_include_pad=True)
          x = F.avg_pool1d(x, kernel_size=4, stride=1, padding=2, ceil_mode=False, count_include_pad=False)
          return x
  
  def test():
      net = Model()
      net.eval()
  
      torch.manual_seed(0)
      x = torch.rand(1, 12, 128)
  
      a = net(x)
  
      # export torchscript
      mod = torch.jit.trace(net, x)
      mod.save("test_F_avg_pool1d.pt")
  
      # torchscript to pnnx
      import os
      os.system("../src/pnnx test_F_avg_pool1d.pt inputshape=[1,12,128]")
  
      # pnnx inference
      import test_F_avg_pool1d_pnnx
      b = test_F_avg_pool1d_pnnx.test_inference()
  
      return torch.equal(a, b)
  
  if __name__ == "__main__":
      if test():
          exit(0)
      else:
          exit(1)
  
apis: torch.manual_seed torch.rand torch.equal torch.nn.functional.avg_pool1d torch.jit.trace