class SelfAttention(nn.Module): def __init__(self, in_channels, reduction=4): super(SelfAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool1d(1) # print("in_channels", in_channels) 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) print("y.shape=", y.shape) # print("in_channels", in_channels) y = self.fc1(y) y = self.relu(y) y = self.fc2(y) y = self.sigmoid(y) return x * y.expand_as(x)我想在这个类中实现将传入的参数进行打印，你可以给我详细的代码吗？

你可以在 `__init__` 方法中添加以下代码：

print("in_channels:", in_channels)
print("reduction:", reduction)

在 `forward` 方法中添加以下代码：

print("x.shape:", x.shape)

这样就可以在打印的信息中看到传入的参数和输入的张量形状了。完整的代码如下：

class SelfAttention(nn.Module):
    def __init__(self, in_channels, reduction=4):
        super(SelfAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool1d(1) #
        print("in_channels:", in_channels)
        print("reduction:", reduction)
        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):
        print("x.shape:", x.shape)
        b, c, n = x.size()
        y = self.avg_pool(x)
        print("y.shape=", y.shape) #
        y = self.fc1(y)
        y = self.relu(y)
        y = self.fc2(y)
        y = self.sigmoid(y)
        return x * y.expand_as(x)