请给出多id，多标签时序二分类问题的代码

下面是一个基于Pytorch的多id、多标签时序二分类问题的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

class MultiLabelDataset(Dataset):
    def __init__(self, data, max_seq_length):
        self.data = data
        self.max_seq_length = max_seq_length
        
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        # 获取文本和标签
        text = self.data[idx]['text']
        labels = self.data[idx]['labels']
        
        # 将文本转换为词向量，并根据max_seq_length填充或截断
        text_vector = []
        for word in text.split():
            if word in word2vec:
                text_vector.append(word2vec[word])
            else:
                text_vector.append(word2vec['unk'])
        if len(text_vector) < self.max_seq_length:
            text_vector += [word2vec['pad']] * (self.max_seq_length - len(text_vector))
        else:
            text_vector = text_vector[:self.max_seq_length]
        
        # 将标签转换为多标签二进制形式
        label_vector = []
        for label in labels:
            label_vector.append(int(label in label_set))
        
        return torch.tensor(text_vector), torch.tensor(label_vector)

class MultiLabelLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(MultiLabelLSTM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        lstm_out, _ = self.lstm(x.view(len(x), 1, -1))
        out = self.fc(lstm_out.view(len(x), -1))
        return out

# 定义超参数
max_seq_length = 100
input_size = 300
hidden_size = 128
output_size = len(label_set)

# 加载词向量和数据
word2vec = load_word2vec('word2vec.bin')
train_data = load_train_data('train_data.json')
valid_data = load_valid_data('valid_data.json')

# 定义数据集和数据加载器
train_dataset = MultiLabelDataset(train_data, max_seq_length)
valid_dataset = MultiLabelDataset(valid_data, max_seq_length)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
valid_loader = DataLoader(valid_dataset, batch_size=32, shuffle=False)

# 定义模型和优化器
model = MultiLabelLSTM(input_size, hidden_size, output_size)
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    train_loss = 0
    valid_loss = 0
    train_acc = 0
    valid_acc = 0
    
    # 训练模式
    model.train()
    for batch_text, batch_label in train_loader:
        optimizer.zero_grad()
        batch_text = batch_text.float().to(device)
        batch_label = batch_label.float().to(device)
        output = model(batch_text)
        loss = nn.BCEWithLogitsLoss()(output, batch_label)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        train_acc += ((torch.sigmoid(output) > 0.5).int() == batch_label.int()).all(dim=1).float().mean().item()
    train_loss /= len(train_loader)
    train_acc /= len(train_loader)
    
    # 验证模式
    model.eval()
    with torch.no_grad():
        for batch_text, batch_label in valid_loader:
            batch_text = batch_text.float().to(device)
            batch_label = batch_label.float().to(device)
            output = model(batch_text)
            loss = nn.BCEWithLogitsLoss()(output, batch_label)
            valid_loss += loss.item()
            valid_acc += ((torch.sigmoid(output) > 0.5).int() == batch_label.int()).all(dim=1).float().mean().item()
        valid_loss /= len(valid_loader)
        valid_acc /= len(valid_loader)
    
    print('Epoch: {} Train Loss: {:.3f} Train Acc: {:.3f} Valid Loss: {:.3f} Valid Acc: {:.3f}'.format(
        epoch+1, train_loss, train_acc, valid_loss, valid_acc))

其中，`MultiLabelDataset`类用于加载数据集，`MultiLabelLSTM`类用于定义模型，`train_loader`和`valid_loader`用于加载训练集和验证集。在训练模型时，我们使用`nn.BCEWithLogitsLoss()`作为损失函数，输出层不需要使用sigmoid激活函数，因为`nn.BCEWithLogitsLoss()`已经对输出进行了sigmoid处理。我们还计算了训练集和验证集的准确率，以便监控模型的性能。