稀疏多标签图像分类pytorch应该使用什么损失函数

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

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc = nn.Linear(in_features=256, out_features=10)  # 假设输入大小为256，输出类别数为10

    def forward(self, x):
        x = self.fc(x)
        return x

# 定义损失函数
criterion = nn.BCEWithLogitsLoss()

# 初始化模型和优化器
model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练过程
for epoch in range(num_epochs):
    for inputs, labels in dataloader:  # 假设使用dataloader加载数据
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()