在pytorch模型中获取权重和偏差并将其复制到另一个模型中的类似层的正确方法是什么？

source_model_weights = source_model.state_dict()
target_layer_weights = source_model_weights['target_layer.weight']
target_layer_bias = source_model_weights['target_layer.bias']

target_model.load_state_dict({ 'target_layer.weight': target_layer_weights, 'target_layer.bias': target_layer_bias })

import torch
import torch.nn as nn

# 定义源模型和目标模型
source_model = nn.Sequential(
    nn.Linear(10, 20),
    nn.ReLU(),
    nn.Linear(20, 30)
)

target_model = nn.Sequential(
    nn.Linear(10, 20),
    nn.ReLU(),
    nn.Linear(20, 30)
)

# 获取源模型中目标层的权重和偏差
source_model_weights = source_model.state_dict()
target_layer_weights = source_model_weights['1.weight']
target_layer_bias = source_model_weights['1.bias']

# 将权重和偏差复制到目标模型的相应层中
target_model.load_state_dict({ '1.weight': target_layer_weights, '1.bias': target_layer_bias })

# 打印目标模型的参数
print(target_model)