Are there any tutorials on how to add custom layers to pruning?

[ensiwalk]

`class LayerNormFunction(torch.autograd.Function):

@staticmethod
def forward(ctx, x, weight, bias, eps):
    ctx.eps = eps
    N, C, H, W = x.size()
    mu = x.mean(1, keepdim=True)
    var = (x - mu).pow(2).mean(1, keepdim=True)
    y = (x - mu) / (var + eps).sqrt()
    ctx.save_for_backward(y, var, weight)
    y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
    return y

@staticmethod
def backward(ctx, grad_output):
    eps = ctx.eps

    N, C, H, W = grad_output.size()
    y, var, weight = ctx.saved_variables
    g = grad_output * weight.view(1, C, 1, 1)
    mean_g = g.mean(dim=1, keepdim=True)

    mean_gy = (g * y).mean(dim=1, keepdim=True)
    gx = 1. / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
    return gx, (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), grad_output.sum(dim=3).sum(dim=2).sum(
        dim=0), None

class LayerNorm2d(nn.Module):

def __init__(self, channels, eps=1e-6):
    super(LayerNorm2d, self).__init__()
    self.register_parameter('weight', nn.Parameter(torch.ones(channels)))
    self.register_parameter('bias', nn.Parameter(torch.zeros(channels)))
    self.eps = eps

def forward(self, x):
    return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)`