写一段代码使用pytorch实现lstm，用于预测碳排放交易价格，其中价格h受a,b,c,d,e,f,g,h数据的影响，要求通过abcdefgh来预测h

import torch
import torch.nn as nn
import pandas as pd

# Load data from CSV file
data = pd.read_csv('carbon_emission_data.csv')

# Separate features and target variable
X = data[['a', 'b', 'c', 'd', 'e', 'f', 'g']].values
y = data['h'].values

# Normalize data
X = (X - X.mean()) / X.std()
y = (y - y.mean()) / y.std()

# Convert data to tensors
X = torch.tensor(X, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.float32)

# Define LSTM model
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(LSTM, self).__init__()
self.hidden_size = hidden_size
self.lstm = nn.LSTM(input_size, hidden_size)
self.linear = nn.Linear(hidden_size, output_size)

def forward(self, input):
lstm_out, _ = self.lstm(input.view(len(input), 1, -1))
output = self.linear(lstm_out.view(len(input), -1))
return output[-1]

# Define hyperparameters
input_size = 7
hidden_size = 64
output_size = 1
learning_rate = 0.01
num_epochs = 100

# Initialize model and optimizer
model = LSTM(input_size, hidden_size, output_size)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

# Train model
for epoch in range(num_epochs):
# Forward pass
outputs = model(X)
loss = criterion(outputs, y)

# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()

if (epoch+1) % 10 == 0:
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# Test model
with torch.no_grad():
test_X = torch.tensor([[1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8]], dtype=torch.float32)
predicted_y = model(test_X).item()
print('Predicted carbon emission trading price: {:.4f}'.format(predicted_y))