class SelfAttention(nn.Module): def init(self, in_channels, reduction=4): super(SelfAttention, self).init() self.avg_pool = nn.AdaptiveAvgPool1d(1) self.fc1 = nn.Conv1d(in_channels, in_channels // reduction, 1, bias=False) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv1d(in_channels // reduction, in_channels, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, n = x.size() y = self.avg_pool(x) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x * y.expand_as(x)该类在使用时需要传递哪个参数，调用的语句应该是什么样子的？

在使用时，需要传递输入张量 `x` 的参数。调用语句应该是类实例化后调用 `forward()` 方法，例如：

in_channels = 64
reduction = 4
x = torch.randn(2, in_channels, 10)
self_attention = SelfAttention(in_channels, reduction)
output = self_attention(x)

其中，`x` 是输入张量，大小为 `(batch_size, in_channels, sequence_length)`，`in_channels` 是输入张量的通道数，`reduction` 是降维比例，用于控制输出特征图的通道数。