编写pytorch代码，在CPSO优化算法优化LSTMAttention模型过程中，用训练集的预测误差当作每个粒子的适应度值，用于LSTMAttention模型评估粒子当前位置的优劣程度，选择最优的粒子作为当前的全局最优解，以及每个粒子自身的局部最优解，根据全局最优解和局部最优解，更新每个粒子的速度和位置，以及更新模型参数。最后，根据收敛条件是否满足，决定是否继续迭代，如果达到预设的最大迭代次数或误差已经足够小，则停止迭代，并返回最优的LSTM模型参数。

以下是一个简单的用Pytorch实现CPSO优化LSTMAttention模型的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import random

# 定义LSTMAttention模型
class LSTMAttention(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(LSTMAttention, self).__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True)
        self.linear = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h, _ = self.lstm(x)
        h = torch.tanh(h)
        alpha = torch.softmax(self.linear(h), dim=1)
        c = torch.bmm(alpha.permute(0, 2, 1), h).squeeze(1)
        return c

# 定义适应度函数
def fitness_func(model, trainX, trainY):
    model.eval()
    with torch.no_grad():
        preds = model(trainX)
        loss = nn.MSELoss()(preds, trainY)
    return loss.item()

# 定义CPSO算法的类
class CPSO:
    def __init__(self, input_dim, hidden_dim, output_dim, n, max_iter, w, c1, c2, trainX, trainY):
        self.dim = input_dim*hidden_dim + hidden_dim**2 + hidden_dim*output_dim  # 粒子的维度
        self.n = n  # 粒子数
        self.max_iter = max_iter  # 最大迭代次数
        self.w = w  # 惯性权重
        self.c1 = c1  # 学习因子1
        self.c2 = c2  # 学习因子2
        self.x = Variable(torch.rand(n, self.dim), requires_grad=True)  # 粒子的位置
        self.v = Variable(torch.rand(n, self.dim), requires_grad=True)  # 粒子的速度
        self.pbest = self.x.clone()  # 个体最优解
        self.gbest = None  # 全局最优解
        self.pbest_fit = fitness_func(self.get_model(), trainX, trainY)  # 个体最优解的适应度值
        self.gbest_fit = None  # 全局最优解的适应度值
        self.trainX = trainX
        self.trainY = trainY

    # 获取当前粒子的模型参数
    def get_model(self):
        input_dim = 4
        hidden_dim = 8
        output_dim = 1
        start = 0
        end = input_dim*hidden_dim
        W_xh = self.x[:, start:end].view(self.n, input_dim, hidden_dim)
        start = end
        end += hidden_dim**2
        W_hh = self.x[:, start:end].view(self.n, hidden_dim, hidden_dim)
        start = end
        end += hidden_dim*output_dim
        W_hy = self.x[:, start:end].view(self.n, hidden_dim, output_dim)
        models = []
        for i in range(self.n):
            model = LSTMAttention(input_dim, hidden_dim, output_dim)
            model.lstm.weight_ih_l0.data = W_xh[i]
            model.lstm.weight_hh_l0.data = W_hh[i]
            model.linear.weight.data = W_hy[i]
            models.append(model)
        return models

    # CPSO算法的优化过程
    def optimize(self):
        for i in range(self.max_iter):
            r1 = Variable(torch.rand(self.n, self.dim), requires_grad=True)
            r2 = Variable(torch.rand(self.n, self.dim), requires_grad=True)
            self.v = self.w*self.v + self.c1*r1*(self.pbest-self.x) + self.c2*r2*(self.gbest-self.x)
            self.x = self.x + self.v
            fit = []
            models = self.get_model()
            for j in range(self.n):
                loss = fitness_func(models[j], self.trainX, self.trainY)
                fit.append(loss)
                if loss < self.pbest_fit[j]:
                    self.pbest[j] = self.x[j]
                    self.pbest_fit[j] = loss
            if self.gbest_fit is None or min(fit) < self.gbest_fit:
                self.gbest, self.gbest_fit = self.x[fit.index(min(fit))], min(fit)
            for j in range(self.n):
                if fit[j] >= self.pbest_fit[j]:
                    continue
                start = 0
                end = input_dim*hidden_dim
                W_xh = self.x[j, start:end].view(input_dim, hidden_dim)
                start = end
                end += hidden_dim**2
                W_hh = self.x[j, start:end].view(hidden_dim, hidden_dim)
                start = end
                end += hidden_dim*output_dim
                W_hy = self.x[j, start:end].view(hidden_dim, output_dim)
                model = LSTMAttention(input_dim, hidden_dim, output_dim)
                model.lstm.weight_ih_l0.data = W_xh
                model.lstm.weight_hh_l0.data = W_hh
                model.linear.weight.data = W_hy
                optimizer = optim.Adam(model.parameters(), lr=0.01)
                for k in range(100):
                    optimizer.zero_grad()
                    preds = model(trainX)
                    loss = nn.MSELoss()(preds, trainY)
                    loss.backward()
                    optimizer.step()
                self.x[j, :end] = model.lstm.weight_ih_l0.data.view(-1)
                self.x[j, start:end] = model.lstm.weight_hh_l0.data.view(-1)
                self.x[j, start:end] = model.linear.weight.data.view(-1)

            # 判断是否收敛
            if self.gbest_fit < 0.01:
                break

# 测试CPSO优化LSTMAttention模型
if __name__ == '__main__':
    input_dim = 4
    hidden_dim = 8
    output_dim = 1
    n = 20
    max_iter = 100
    w = 0.8
    c1 = 2.0
    c2 = 2.0
    trainX = Variable(torch.rand(100, 10, input_dim))
    trainY = Variable(torch.rand(100, output_dim))
    cpso = CPSO(input_dim, hidden_dim, output_dim, n, max_iter, w, c1, c2, trainX, trainY)
    cpso.optimize()
    best_model = LSTMAttention(input_dim, hidden_dim, output_dim)
    best_model.lstm.weight_ih_l0.data = cpso.gbest[:input_dim*hidden_dim].view(input_dim, hidden_dim)
    best_model.lstm.weight_hh_l0.data = cpso.gbest[input_dim*hidden_dim:(input_dim+hidden_dim)*hidden_dim].view(hidden_dim, hidden_dim)
    best_model.linear.weight.data = cpso.gbest[(input_dim+hidden_dim)*hidden_dim:].view(hidden_dim, output_dim)
    print(best_model)

在这个例子中，我们定义了一个LSTMAttention模型，同时定义了适应度函数用于衡量每个粒子的适应度值。在CPSO算法的优化过程中，我们使用训练集的预测误差作为每个粒子的适应度值，根据全局最优解和局部最优解，更新每个粒子的速度和位置，以及更新模型参数。在测试的时候，我们输出了最优的LSTMAttention模型参数。