text: import torch
  from torch.nn import functional as F
  
  # 1.初始化
  input = torch.tensor(
      [
          [1,1,1,1,1],
          [1,1,1,1,1],
          [0,0,0,1,1],
          [1,1,1,1,1]
          
      ]).unsqueeze(0).float()
  # print(input.size())#torch.Size([1, 4, 5])
  # print(input)
  
  #2. avg_pool1d
  # m1 = F.avg_pool1d(input,kernel_size=2)
  # print(m1)#tensor([[[1.0000, 1.0000],
  #          # [1.0000, 1.0000],
  #          # [0.0000, 0.5000],
  #          # [1.0000, 1.0000]]])
   
  #3. avg_pool2d
  # m = F.avg_pool2d(input,kernel_size=2)#这里是在原矩阵中找出2*2的区域求平均，不够的舍弃，stride=(2,2)
  # print(m)#tensor([[[1.0000, 1.0000],
  #          # [0.5000, 0.7500]]])
  
  
  # m3= F.avg_pool2d(input,kernel_size=2,stride=1)#这里是在原矩阵中找出2*2的区域求平均，不够的舍弃，stride=(1,1),[1,1,4,5]-->[1,1,3,4]
  # print(m3)#tensor([[[1.0000, 1.0000, 1.0000, 1.0000],
  #          # [0.5000, 0.5000, 0.7500, 1.0000],
  #          # [0.5000, 0.5000, 0.7500, 1.0000]]])
  
  c = torch.randn(256,384,4,4)
  print(c.size())
  out = F.avg_pool2d(c, 4)
  print(out.size())
  print("out.size(0)",out.size(0))
  out = out.view(out.size(1),-1)
  out.size()