编写pytorch代码，要求定义LSTMAttention模型，定义FA-CPSO优化算法，初始化粒子群，初始化最优解和粒子适应度、设置混沌常数和萤火虫扰动常数、初始化萤火虫位置和亮度、计算初始全局最优参数、计算萤火虫的初始亮度、开始迭代、更新亮度、更新每个粒子的适应度位置和速度、计算每个粒子的适应度、更新全局最优参数，优化LSTMAttention模型的权重参数，将最佳参数加载到模型中，读取训练集trainset中的数据进行训练

好的，这是一个比较复杂的任务，我将为你提供一些代码框架和思路，你可以根据实际情况进行修改和完善。

首先，你需要定义LSTMAttention模型。这里以一个简单的模型为例：

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.attention = nn.Linear(hidden_dim, 1)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        attention_scores = self.attention(lstm_out)
        attention_weights = torch.softmax(attention_scores, dim=1)
        context_vector = torch.sum(attention_weights * lstm_out, dim=1)
        output = self.fc(context_vector)
        return output

接下来，你需要定义FA-CPSO优化算法。这里以一个比较简单的实现为例：

class FACPSO():
    def __init__(self, n_particles, n_dim, n_iter):
        self.n_particles = n_particles
        self.n_dim = n_dim
        self.n_iter = n_iter

        self.w = 0.729
        self.c1 = 1.49445
        self.c2 = 1.49445

        self.x = torch.randn(n_particles, n_dim)
        self.v = torch.randn(n_particles, n_dim)

        self.pbest_x = self.x.clone()
        self.pbest_fitness = torch.zeros(n_particles)

        self.gbest_x = torch.randn(n_dim)
        self.gbest_fitness = float('inf')

    def optimize(self, fitness_func):
        for i in range(self.n_iter):
            r1 = torch.rand(self.n_particles, self.n_dim)
            r2 = torch.rand(self.n_particles, self.n_dim)
            v_new = self.w * self.v + self.c1 * r1 * (self.pbest_x - self.x) + self.c2 * r2 * (self.gbest_x - self.x)
            x_new = self.x + v_new
            fitness_new = fitness_func(x_new)

            update = fitness_new < self.pbest_fitness
            self.pbest_x[update] = x_new[update]
            self.pbest_fitness[update] = fitness_new[update]

            update = self.pbest_fitness.min