torch.sqrt(torch.sum(torch.pow(wight, 2), dim=[1, 2, 3]))

This code computes the L2 norm (Euclidean distance) of a weight tensor along each channel, height and width dimension. Specifically, it first computes the element-wise square of the weight tensor using torch.pow(), then sums over all elements along the channel, height and width dimensions using dim=[1, 2, 3], and finally takes the element-wise square root of the result using torch.sqrt().

The resulting tensor will have the same shape as the first dimension of the weight tensor, representing the L2 norm of each filter in a convolutional neural network. This is often used as a regularization term to encourage sparsity of the learned filters, as smaller L2 norms indicate smaller weights and therefore fewer active neurons in the network.