我听说在做反向传播的时候，权值会用学习后期和偏导数来更新。
但是我不知道在向后传播代码中的learning_late参数应该放在哪里，而且我想知道如果没有学习延迟的设置，默认的学习延迟是什么？
所以，这里是代码，我想学习.

import torch
import torch.nn as nn
import torch.optim as optim

class MyNeuralNetwork(nn.Module):
    def __init__(self):
        super(MyNeuralNetwork, self).__init__()
        layer_1=nn.Linear(in_features=2, out_features=2, bias=False)
        weight_1 = torch.tensor([[.3,.25],[.4, .35]])
        
        layer_1.weight = nn.Parameter(weight_1)
        self.layer1 = nn.Sequential(
            layer_1,
            nn.Sigmoid()
        )
        
        layer_2 = nn.Linear(in_features=2, out_features=2, bias=False)
        weight_2 = torch.tensor([[.45, .4],[.7, .6]])
        
        layer_2.weight = nn.Parameter(weight_2)
        self.layer2 = nn.Sequential(
            layer_2,
            nn.Sigmoid()
        )
    
    def forward(self, input):
        output = self.layer1(input)
        output = self.layer2(output)
        
        return output
    
model = MyNeuralNetwork().to("cpu")
print(model)

input = torch.tensor([0.1,0.2]).reshape(1,-1)
target = torch.tensor([0.4,0.6]).reshape(1,-1)

out = model(input)
print(f"output value : {out}")

criterion = nn.MSELoss()
loss = criterion(out, target)
print(f"loss value : {loss}")

model.zero_grad() 
print('↓ layer1.weight before backward propagation ↓')
print(model._modules['layer1']._modules['0'].weight)
print(model._modules['layer2']._modules['0'].weight)
print()

loss.backward() # where can I put the learning late in back propagation.
print('↓ layer1.weight after backward propagation ↓')
print(model._modules['layer1']._modules['0'].weight)
print(model._modules['layer2']._modules['0'].weight)

我的问题的重点是如何添加学习晚，我想为培训这种模式。