写出下面完整的程序：pytorch实现时序预测，用lstm、attention、encoder-decoder和Knowledge Distillation四种技术。注意是完整的代码

import torch
from torch import nn
from torch.nn import functional as F# 定义encoder
class Encoder(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, dropout=0, bidirectional=False):
super(Encoder, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bidirectional = bidirectional
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, dropout=dropout, bidirectional=bidirectional)

def forward(self, x, hidden):
output, hidden = self.lstm(x, hidden)
return output, hidden

def init_hidden(self, batch_size):
num_directions = 2 if self.bidirectional else 1
h0 = torch.zeros(self.num_layers * num_directions, batch_size, self.hidden_size)
c0 = torch.zeros(self.num_layers * num_directions, batch_size, self.hidden_size)
return (h0, c0)# 定义Attention
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.hidden_size = hidden_size
self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
self.v = nn.Parameter(torch.rand(hidden_size))
stdv = 1. / math.sqrt(self.v.size(0))
self.v.data.normal_(mean=0, std=stdv)

def forward(self, hidden, encoder_outputs):
# hidden: [batch_size, hidden_size]
# encoder_outputs: [seq_len, batch_size, hidden_size * 2]
seq_len = encoder_outputs.size(0)
# repeat hidden
hidden = hidden.unsqueeze(1).repeat(1, seq_len, 1)
encoder_outputs = encoder_outputs.permute(1, 0, 2)
# hidden: [batch_size, seq_len, hidden_size]
# encoder_outputs: [batch_size, seq_len, hidden_size * 2]
energy = torch.tanh(self.attn(torch.cat([hidden, encoder_outputs], 2)))
# energy: [batch_size, seq_len, hidden_size]
energy = energy.permute(0, 2, 1)
# v: [hidden_size]
v = self.v.repeat(encoder_outputs.size(0), 1).unsqueeze(1)
# v: [batch_size, 1, hidden_size]
# attn_weights: [batch_size, seq_len]
attn_weights = torch.bmm(v, energy).squeeze(1)
return F.softmax(attn_weights, dim=1).unsqueeze(1)# 定义Decoder
class Decoder(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, dropout=0):
super(Decoder, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.attention = Attention(hidden_size)
self.lstm = nn.LSTM(input_size + hidden_size, hidden_size, num_layers, dropout=dropout)
self.out = nn.Linear(hidden_size, input_size)

def forward(self, x, hidden, encoder_outputs):
# x: [batch_size]
# hidden: [num_layers, batch_size, hidden_size]
# encoder_outputs: [seq_len, batch_size, hidden_size * 2]
x = x.unsqueeze(1)
# x: [batch_size, 1]
x = F.relu(self.out(x))
# x: [batch_size, 1, input_size]
seq_len = encoder_outputs.size(0)
context = self.attention(hidden[-1], encoder_outputs)
# context: [batch_size, 1, seq_len]
context = context.repeat(1, x.size(1), 1)
# context: [batch_size, 1, seq_len]
x = torch.cat([x, context], 2)
# x: [batch_size, 1, input_size + seq_len]
output, hidden = self.lstm(x, hidden)
output = output.squeeze(1)
output = F.log_softmax(self.out(output), dim=1)
return output, hidden, context# 定义Knowledge Distillation
class KnowledgeDistillation(nn.Module):
def __init__(self, teacher_model, student_model):
super(KnowledgeDistillation, self).__init__()
self.teacher_model = teacher_model
self.student_model = student_model

def forward(self, x, targets):
teacher_logits, student_logits = self.teacher_model(x), self.student_model(x)
loss = F.kl_div(F.log_softmax(student_logits, dim=1), F.softmax(teacher_logits / 5, dim=1))
distillation_loss = F.cross_entropy(student_logits, targets) + loss
return distillation_loss