获取nn.Sequential未定义的pytorch模型中的一些层

import torch
import torch.nn as nn

class CustomModel(nn.Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        self.layer1 = nn.Linear(10, 20)
        self.layer2 = nn.Linear(20, 30)
        self.layer3 = nn.Linear(30, 40)
    
    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        return x

model = CustomModel()
model.load_state_dict(torch.load('model.pth'))

for name, layer in model.named_children():
    print(name, layer)

params = model.state_dict()
layer1_params = params['layer1.weight']