写出下面的程序：pytorch实现时序预测，用lstm、attention、encoder-decoder和Knowledge Distillation四种技术。

我可以提供一个基本的程序示例，但实际上每个程序可能会有所不同，取决于你想要实现的功能。# 导入pytorch模块
import torch
import torch.nn as nn# 定义LSTM
class LSTM(nn.Module):
def init(self, input_dim, hidden_dim, num_layers):
super(LSTM, self).init()
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers)
self.fc = nn.Linear(hidden_dim, 1)
def forward(self, x):
out, _ = self.lstm(x)
out = self.fc(out[:, -1, :])
return out# 定义Attention模型

class Attention(nn.Module):
def init(self, input_dim, hidden_dim, num_layers):
super(Attention, self).init()
self.attn = nn.Sequential(
nn.Linear(input_dim + hidden_dim, 10),
nn.ReLU(),
nn.Linear(10, 1)
)
def forward(self, x, hidden):
x = torch.cat([x, hidden], dim=1)
out = self.attn(x)
return out# 定义Encoder-Decoder

class EncoderDecoder(nn.Module):
def init(self, input_dim, hidden_dim, num_layers):
super(EncoderDecoder, self).init()
self.encoder = nn.LSTM(input_dim, hidden_dim, num_layers)
self.decoder = nn.LSTM(input_dim, hidden_dim, num_layers)
self.fc = nn.Linear(hidden_dim, 1)
def forward(self, x):
out, _ = self.encoder(x)
_, out = self.decoder(out)
out = self.fc(out[:, -1, :])
return out# 定义Knowledge Distillation

class KnowledgeDistillation(nn.Module):
def init(self, input_dim, hidden_dim, num_layers):
super(KnowledgeDistillation, self).init()
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers)
self.fc = nn.Linear(hidden_dim, 1)
self.teacher_fc = nn.Linear(hidden_dim, 1)
self.kd_loss = nn.KLDivLoss()
def forward(self, x, teacher_pred):
out, _ = self.lstm(x)
out = self.fc(out[:, -1, :])
teacher_out = self.teacher_fc(out[:, -1, :])
kd_loss = self.kd_loss(out, teacher_pred)
return out, kd_loss