给出一段代码，用pytorch实现多维lstm预测价格，并打印输入形状以及输出形状还有标签形状

抱歉，我可以回答这个问题。以下是一个用 PyTorch 实现多维 LSTM 预测价格的代码示例：

import torch
import torch.nn as nn

# 定义 LSTM 模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# 定义输入数据和标签
input_data = torch.randn(10, 5, 3) # 输入形状为 (batch_size, seq_len, input_size)
labels = torch.randn(10, 1) # 标签形状为 (batch_size, output_size)

# 定义模型参数
input_size = 3
hidden_size = 10
num_layers = 2
output_size = 1

# 初始化模型并进行训练
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTM(input_size, hidden_size, num_layers, output_size).to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for epoch in range(100):
    optimizer.zero_grad()
    outputs = model(input_data)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

# 打印输出形状
print(outputs.shape) # 输出形状为 (batch_size, output_size)

# 打印标签形状
print(labels.shape) # 标签形状为 (batch_size, output_size)

在这个示例中，我们定义了一个多维 LSTM 模型，用于预测价格。我们使用 PyTorch 内置的 LSTM 层和线性层来构建模型，并使用均方误差损失函数和 Adam 优化器进行训练。最后，我们打印了输出形状和标签形状，以检查它们是否正确。