请写一段代码使用TCN根据风速、风向、温度、湿度、气压、空气密度数据预测风电场输出功率

请参考以下代码：

import torch
from torch import nn
from torch.nn.utils import weight_norm

class TemporalBlock(nn.Module):
    def __init__(self, input_size, output_size, kernel_size, stride, dilation, padding):
        super(TemporalBlock, self).__init__()
        self.conv1 = weight_norm(nn.Conv1d(input_size, output_size, kernel_size,
                                           stride=stride, padding=padding, dilation=dilation))
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(0.2)
        self.conv2 = weight_norm(nn.Conv1d(output_size, output_size, kernel_size,
                                           stride=stride, padding=padding, dilation=dilation))
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(0.2)
        self.net = nn.Sequential(self.conv1, self.relu1, self.dropout1,
                                  self.conv2, self.relu2, self.dropout2)
        self.downsample = nn.Conv1d(input_size, output_size, 1) if input_size != output_size else None
        self.relu = nn.ReLU()
        
    def forward(self, x):
        y = self.net(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return self.relu(x + y)

class TemporalConvNet(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size=2, dropout=0.2):
        super(TemporalConvNet, self).__init__()
        layers = []
        dilation_size = 1
        for i in range(len(hidden_size)):
            dilation = dilation_size
            if i == len(hidden_size) - 1:
                kernel_size = input_size + i + 1
                stride = 1
                padding = int((kernel_size - 1) * dilation / 2)
            else:
                kernel_size = 2
                stride = 1
                padding = 1
            layers += [TemporalBlock(input_size, hidden_size[i], kernel_size,
                                      stride=stride, dilation=dilation, padding=padding)]
            dilation_size *= 2
        self.network = nn.Sequential(*layers)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        x = x.permute(0, 2, 1)
        x = self.network(x)
        x = x.permute(0, 2, 1)
        x = self.dropout(x)
        return x

class TCN(nn.Module):
    def __init__(self, input_size, output_size, hidden_size, kernel_size, dropout):
        super(TCN, self).__init__()
        self.tcn = TemporalConvNet(input_size, hidden_size, kernel_size, dropout=dropout)
        self.linear = nn.Linear(hidden_size[-1], output_size)
        
    def forward(self, x):
        output = self.tcn(x)
        output = self.linear(output[:, -1, :])
        return output

input_size = 6   # 风速、风向、温度、湿度、气压、空气密度数据
output_size = 1  # 风电场输出功率
hidden_size = [64, 128, 256]  # 隐藏层神经元数量
kernel_size = 2
dropout = 0.2

model = TCN(input_size, output_size, hidden_size, kernel_size, dropout)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

# train and validate function
def train(train_loader):
    model.train()
    train_loss = 0.0
    for i, (data, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() / len(train_loader)
    return train_loss

def validate(val_loader):
    model.eval()
    val_loss = 0.0
    for i, (data, labels) in enumerate(val_loader):
        output = model(data)
        loss = criterion(output, labels)
        val_loss += loss.item() / len(val_loader)
    return val_loss
      
# set up data
train_data = ...
train_labels = ...
val_data = ...
val_labels = ...

train_dataset = torch.utils.data.TensorDataset(train_data, train_labels)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
val_dataset = torch.utils.data.TensorDataset(val_data, val_labels)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=True)

# train and validate
for epoch in range(10):
    train_loss = train(train_loader)
    val_loss = validate(val_loader)
    print('Epoch [{}/{}], train_loss: {:.4f}, val_loss: {:.4f}'
          .format(epoch + 1, 10, train_loss, val_loss))
          
# make prediction
test_data = ...
model.eval()
prediction = model(test_data)
print(prediction)

注：由于没有提供具体的数据集，以上代码仅作为参考，请根据实际数据集进行调整。