编写pytorch代码，读取时序数据集dataset，将数据集通过滑动窗口划分法按照比例为0.7：0.2：0.1划分为训练集、验证集和测试集，将训练集、验证集和测试集中的特征和标签划分开

好的，以下是代码：

import torch
from torch.utils.data import Dataset, DataLoader

class TimeSeriesDataset(Dataset):
    def __init__(self, data):
        self.data = data
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, index):
        return self.data[index]

def sliding_window_split(data, train_ratio=0.7, val_ratio=0.2):
    n = len(data)
    train_size = int(train_ratio * n)
    val_size = int(val_ratio * n)
    test_size = n - train_size - val_size
    
    train_data = data[:train_size]
    val_data = data[train_size:train_size+val_size]
    test_data = data[train_size+val_size:]
    
    return train_data, val_data, test_data

def split_features_labels(data):
    features = data[:, :-1]
    labels = data[:, -1]
    return features, labels

# 读取时序数据集
data = torch.load('time_series_data.pt')

# 将数据集通过滑动窗口划分法按照比例为0.7：0.2：0.1划分为训练集、验证集和测试集
train_data, val_data, test_data = sliding_window_split(data, train_ratio=0.7, val_ratio=0.2)

# 将训练集、验证集和测试集中的特征和标签划分开
train_features, train_labels = split_features_labels(train_data)
val_features, val_labels = split_features_labels(val_data)
test_features, test_labels = split_features_labels(test_data)

# 将数据封装成PyTorch的Dataset和DataLoader
train_dataset = TimeSeriesDataset(torch.utils.data.TensorDataset(train_features, train_labels))
val_dataset = TimeSeriesDataset(torch.utils.data.TensorDataset(val_features, val_labels))
test_dataset = TimeSeriesDataset(torch.utils.data.TensorDataset(test_features, test_labels))

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

其中，`sliding_window_split`函数实现了滑动窗口划分法，`split_features_labels`函数实现了将特征和标签划分开的功能。最后，我们将训练集、验证集和测试集封装成了PyTorch的Dataset和DataLoader。