Normalize# class myoverse.transforms.Normalize(p=2.0, dim='channel', eps=1e-08, **kwargs)[source]# L-p normalization along a dimension. Parameters: p (float) – Norm type (1=L1, 2=L2/Euclidean, inf=max). dim (str) – Dimension to normalize over. eps (float) – Small value to avoid division by zero. Examples >>> x = torch.randn(64, 2048, names=('channel', 'time')) >>> norm = Normalize(p=2, dim='channel') >>> y = norm(x) # L2 normalized along channels Methods __init__([p, dim, eps]) _apply(x) Apply the transform.