I’m unsure what the ShufflingLayer would exactly do, but assuming you would permute the input activations, you could most likely write a custom nn.Module and apply e.g.:
def forward(self, x):
    x = x[:, torch.randperm(x.size(1))]
    return x

to shuffle the data in dim1.