One way to implement custom activation functions in PyTorch is by creating a custom module and defining the desired activation function inside it. You can then use this custom module like any other PyTorch module in your neural network.

Another approach is to use the torch.nn.functional module, which provides a wide range of built-in activation functions. You can define your custom activation function as a combination of these existing functions, using torch operations to achieve the desired functionality.

PyTorch also allows you to define activation functions using the autograd mechanism. By subclassing the torch.autograd.Function class and implementing the forward and backward methods, you can create a custom activation function with full support for automatic differentiation.