关于raise NotImplementedError报错

import torch
from torch import nn
import torch.nn.functional as F

class BasicConv2d(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size,padding=0):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels,out_channels,kernel_size,padding=padding)
        self.bn = nn.BatchNorm2d(out_channels,eps=0.001)

    def _slow_forward(self,x):
        x = self.conv(x)
        y = self.bn(x)
        return F.relu(y,inplace=True)

class Inception_v2(nn.Module):
    def __init__(self):
        super(Inception_v2, self).__init__()
        #对应1 x 1卷积分支
        self.branch1 = BasicConv2d(192,96,1,0)
        #对应1 x 1卷积与3 x 3卷积分支
        self.branch2 = nn.Sequential(
            BasicConv2d(192, 48, 1, 0),
            BasicConv2d(48 , 64, 3, 1)
        )
        #对应1 x 1卷积、3 x 3卷积与3 x 3卷积分支
        self.branch3 = nn.Sequential(
            BasicConv2d(192,64,1,0),
            BasicConv2d(64, 96, 3, 1),
            BasicConv2d(96, 96, 3, 1)
        )
        #对应3 x 3 POOLING
        self.branch4 = nn.Sequential(
            nn.AvgPool2d(3,stride=1,padding=1,count_include_pad=False),
            BasicConv2d(192,64,1,0)
        )
        #前向过程，将4个分支进行torch.cat()拼接起来
    def forward(self,x):
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        x4 = self.branch4(x)
        out = torch.cat((x1,x2,x3,x4),1)
        return out


#测试
net_inceptionv2 = Inception_v2()
# print(net_inceptionv2)
input = torch.randn([1,192,32,32])
print(input.shape)
output = net_inceptionv2(input)
print(output.shape)

一直出现下面的错误：

问题：
在继承父类，需要重写父类的forward()函数，然而我写成了_slow_forward()函数：

 def forward(self,x):
        x = self.conv(x)
        x = self.bn(x)
        return F.relu(x,inplace=True)

总结：
一般出现 raise NotImplementedError 的错误的时候，都是子类没有重写父类中的成员成员函数，然后子类对象调用该函数时，会提示这个错误！

（俺就是记录一下，学习中…/狗头.jpg）