编写pytorch代码，定义LSTMAttention模型，定义一个CPSO优化算法，定义一个适应度函数fitness_function，用于评估LSTMAttention模型在给定超参数下的性能，用CPSO算法优化模型中神经元个数、dropout、batch_size、自注意力等超参数，找到最优的超参数组合，并将其传递给模型。

好的，我可以帮你回答这个问题。

首先，我们需要导入使用的库：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random

接下来，我们定义LSTMAttention模型：

class LSTMAttention(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, dropout):
        super(LSTMAttention, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.dropout = dropout
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout, bidirectional=True)
        self.fc = nn.Linear(hidden_size*2, 1)
        self.softmax = nn.Softmax(dim=1)
        
    def forward(self, x):
        h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        attn_weights = self.softmax(self.fc(out))
        attn_applied = torch.bmm(attn_weights.transpose(1,2), out)
        return attn_applied

在这个模型中，我们使用了LSTM和自注意力机制。其中，LSTM负责对输入进行编码，自注意力机制则负责对LSTM的输出进行加权平均，以提取输入中最重要的特征。这个模型的输入为一个batch的序列，输出为加权平均后的特征。

接下来，我们定义CPSO算法：

class CPSO():
    def __init__(self, fitness_function, num_particles, num_iterations, num_dimensions, min_values, max_values, c1=2.0, c2=2.0, w=0.7):
        self.fitness_function = fitness_function
        self.num_particles = num_particles
        self.num_iterations = num_iterations
        self.num_dimensions = num_dimensions
        self.min_values = min_values
        self.max_values = max_values
        self.c1 = c1
        self.c2 = c2
        self.w = w
        self.particles_position = np.zeros((self.num_particles, self.num_dimensions))
        self.particles_velocity = np.zeros((self.num_particles, self.num_dimensions))
        self.global_best_position = np.zeros((1, self.num_dimensions))
        self.global_best_fitness = float('inf')
        self.particles_fitness = np.zeros(self.num_particles)

    def initialize_particles(self):
        for i in range(self.num_particles):
            for d in range(self.num_dimensions):
                self.particles_position[i, d] = random.uniform(self.min_values[d], self.max_values[d])
            self.particles_fitness[i] = self.fitness_function(self.particles_position[i, :])
            if self.particles_fitness[i] < self.global_best_fitness:
                self.global_best_fitness = self.particles_fitness[i]
                self.global_best_position = self.particles_position[i, :]

    def update_particles_position(self):
        for i in range(self.num_particles):
            for d in range(self.num_dimensions):
                self.particles_position[i, d] = self.particles_position[i, d] + self.particles_velocity[i, d]
                if self.particles_position[i, d] > self.max_values[d]:
                    self.particles_position[i, d] = self.max_values[d]
                if self.particles_position[i, d] < self.min_values[d]:
                    self.particles_position[i, d] = self.min_values[d]

    def update_particles_velocity(self):
        for i in range(self.num_particles):
            r1 = random.uniform(0, 1)
            r2 = random.uniform(0, 1)
            for d in range(self.num_dimensions):
                cognitive_component = self.c1 * r1 * (self.particles_best_position[i, d] - self.particles_position[i, d])
                social_component = self.c2 * r2 * (self.global_best_position[0, d] - self.particles_position[i, d])
                self.particles_velocity[i, d] = self.w * self.particles_velocity[i, d] + cognitive_component + social_component

    def optimize(self):
        self.initialize_particles()
        for i in range(self.num_iterations):
            self.update_particles_velocity()
            self.update_particles_position()
            for j in range(self.num_particles):
                self.particles_fitness[j] = self.fitness_function(self.particles_position[j, :])
                if self.particles_fitness[j] < self.global_best_fitness:
                    self.global_best_fitness = self.particles_fitness[j]
                    self.global_best_position = self.particles_position[j, :]

在这个算法中，我们使用了粒子群优化算法来搜索LSTMAttention模型的最优超参数组合。其中，每个粒子代表一个超参数组合。在每次迭代中，每个粒子会根据自己的速度移动，并更新自己的位置和适应度。同时，全局最优位置和适应度也会被更新。

最后，我们定义适应度函数fitness_function，用于评估LSTMAttention模型在给定超参数下的性能：

def fitness_function(hyperparameters):
    hidden_size = int(hyperparameters[0])
    dropout = hyperparameters[1]
    batch_size = int(hyperparameters[2])
    num_layers = int(hyperparameters[3])
    model = LSTMAttention(input_size, hidden_size, num_layers, dropout)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    model.to(device)
    for epoch in range(num_epochs):
        for i, data in enumerate(train_loader, 0):
            inputs, labels = data
            inputs = inputs.to(device)
            labels = labels.to(device)
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels.unsqueeze(1))
            loss.backward()
            optimizer.step()
    with torch.no_grad():
        total_loss = 0
        for i, data in enumerate(test_loader, 0):
            inputs, labels = data
            inputs = inputs.to(device)
            labels = labels.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, labels.unsqueeze(1))
            total_loss += loss.item()
        avg_loss = total_loss / len(test_loader)
    return avg_loss

在这个函数中，我们首先根据给定的超参数组合构建LSTMAttention模型。然后，我们使用均方误差作为损失函数，使用Adam作为优化器，对模型进行训练。最后，我们在测试集上计算模型的损失，并将其作为适应度函数的返回值。

综上所述，我们可以将以上代码组合起来，得到一个完整的求解LSTMAttention模型最优超参数的程序。