将网络的输出层设计成多个二分类的节点，每个节点对应一个二分类问题，输出值为0或1，然后将每个节点的输出组合成一个01序列。给出torch代码实现

下面是一个用PyTorch实现将网络的输出层设计成多个二分类节点的代码示例：

import torch
import torch.nn as nn

class BinaryOutputNet(nn.Module):
    def __init__(self, input_size, num_classes):
        super(BinaryOutputNet, self).__init__()
        self.fc1 = nn.Linear(input_size, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, num_classes)
        self.sigmoid = nn.Sigmoid()
        
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.sigmoid(self.fc3(x))
        return x

input_size = 10
num_classes = 8
model = BinaryOutputNet(input_size, num_classes)
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# train the model
for epoch in range(10):
    for i in range(100):
        # generate random input and target data
        x = torch.randn(1, input_size)
        y = torch.zeros(1, num_classes)
        for j in range(num_classes):
            y[0][j] = torch.randint(0, 2, (1,))
        # forward pass
        output = model(x)
        loss = criterion(output, y)
        # backward pass and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, loss.item()))

# test the model
x_test = torch.randn(1, input_size)
with torch.no_grad():
    output = model(x_test)
    # convert binary outputs to 01 sequence
    output_seq = ''
    for i in range(num_classes):
        if output[0][i] < 0.5:
            output_seq += '0'
        else:
            output_seq += '1'
    print('Binary outputs:', output)
    print('01 sequence:', output_seq)

在这个例子中，我们将输出层设计成了8个二分类的节点，每个节点对应一个输出位。在训练过程中，我们随机生成输入数据和目标二进制数据，然后使用BCELoss作为损失函数进行训练。在测试过程中，我们将模型的输出转换成01序列并输出。