Actually, you’re right the error does go away if I get the dimensions right:
norm = transforms.Normalize((30, 30, 30, 30), (20, 25, 30, 35))
x = torch.randn(4, 589, 4)
out = norm(x)

But I don’t think this is applying the normalization correctly.  The data from my data loader is shaped [batch_size, seq_length, x_dim] so the scaling should be applied to the last dimension, whereas I think normalize is applying the scaling across the first dimension (the set of image colour maps).