如何向pytorch中的一个网络输出通道添加可学习的偏置

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.bias = nn.Parameter(torch.Tensor(out_channels))  # 定义可学习的偏置

    def forward(self, x):
        x = self.conv(x)
        x += self.bias.view(1, -1, 1, 1)  # 将偏置添加到输出通道
        return x

net = Net()
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)

# 进行训练过程，具体步骤省略