class attention(nn.module): 模块怎么写

以下是一个示例的 `attention` 模块的代码：

import torch
import torch.nn as nn

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, self.hidden_size)
        self.v = nn.Parameter(torch.FloatTensor(1, self.hidden_size))

    def forward(self, hidden, encoder_outputs):
        max_len = encoder_outputs.size(0)
        batch_size = encoder_outputs.size(1)

        energy = torch.zeros(batch_size, max_len)

        if torch.cuda.is_available():
            energy = energy.cuda()

        for i in range(max_len):
            energy[:, i] = self.score(hidden, encoder_outputs[i])

        attention_weights = nn.functional.softmax(energy, dim=1).unsqueeze(1)
        context = attention_weights.bmm(encoder_outputs.transpose(0, 1))

        return context, attention_weights.squeeze(1)

    def score(self, hidden, encoder_output):
        energy = self.attn(torch.cat((hidden, encoder_output), 1))
        energy = energy.transpose(1, 0)
        v = self.v.repeat(encoder_output.size(0), 1).unsqueeze(1)
        energy = torch.bmm(v, energy.unsqueeze(2)).squeeze(2)

        return energy

这个 `attention` 模块通过计算一个加权和来实现对 encoder 输出的注意力。注意力权重是通过对 hidden state 和 encoder 输出进行计算得到的，具体实现可以参考 `score` 方法。最终得到的注意力权重被用于计算加权和，得到注意力上下文。