如何在pytorch中实现帐篷激活功能？

import torch
import torch.nn as nn

class TentActivation(nn.Module):
    def __init__(self):
        super(TentActivation, self).__init__()

    def forward(self, x):
        return torch.max(1 - torch.abs(x), torch.zeros_like(x))

# 使用帐篷激活函数的神经网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10, 5)
        self.tent = TentActivation()
        self.fc2 = nn.Linear(5, 2)

    def forward(self, x):
        x = self.fc1(x)
        x = self.tent(x)
        x = self.fc2(x)
        return x

# 创建模型并进行训练
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 假设有训练数据 train_data 和标签 train_labels
for epoch in range(num_epochs):
    optimizer.zero_grad()
    outputs = model(train_data)
    loss = criterion(outputs, train_labels)
    loss.backward()
    optimizer.step()

# 使用模型进行预测
# 假设有测试数据 test_data
with torch.no_grad():
    predictions = model(test_data)
    predicted_classes = torch.argmax(predictions, dim=1)