帮我写一段稀疏注意力机制的代码，用pytorch架构

好的，为您提供一份基于PyTorch的稀疏注意力机制的代码，如下所示：

import torch
import torch.nn.functional as F

class SparseAttention(torch.nn.Module):

    def __init__(self, in_features, out_features):
        super(SparseAttention, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = torch.nn.Parameter(torch.Tensor(in_features, out_features))
        self.bias = torch.nn.Parameter(torch.Tensor(out_features))
        self.reset_parameters()

    def reset_parameters(self):
        torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight)
        bound = 1 / math.sqrt(fan_in)
        torch.nn.init.uniform_(self.bias, -bound, bound)

    def forward(self, input, indices, values):
        weight = F.linear(input, self.weight, self.bias)

        values = values * weight
        attn_values = scatter_add(src=values, index=indices, dim=0)
        attn_output = attn_values / scatter_add(src=weight, index=indices, dim=0).clamp_min(1e-16)

        return attn_output

希望能够帮助到您！