torch.nn.functional.prelu# torch.nn.functional.prelu(input, weight) → Tensor# Applies element-wise the function PReLU(x)=max(0,x)+weight∗min(0,x)\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)PReLU(x)=max(0,x)+weight∗min(0,x) where weight is a learnable parameter. Note weight is expected to be a scalar or 1-D tensor. If weight is 1-D, its size must match the number of input channels, determined by input.size(1) when input.dim() >= 2, otherwise 1. In the 1-D case, note that when input has dim > 2, weight can be expanded to the shape of input in a way that is not possible using normal broadcasting semantics. See PReLU for more details.