我可以设置张量的一部分不可训练吗？

import torch
import torch.nn as nn

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.fc = nn.Linear(256 * 8 * 8, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

model = MyModel()

# 冻结前两层的参数
for param in model.conv1.parameters():
    param.requires_grad = False
for param in model.conv2.parameters():
    param.requires_grad = False

# 检查哪些参数是可训练的
for name, param in model.named_parameters():
    if param.requires_grad:
        print(f"Trainable parameter: {name}")
    else:
        print(f"Non-trainable parameter: {name}")

optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=0.01)