My solution:
class Model(nn.Module):
    def __init__():
        pass
    
    def forward(inputs, index):
        inputs = inputs[int(index[0]) : int(index[-1]) + 1]
        inputs = [torch.from_numpy(i).to(index.device) for i in inputs]
        ...

model = nn.DataParalle(Model())
index = torch.tensor(list(range(batch_size))).to(device)
x = [ndarray1, ndarray2, ..., ndarray16]
out = model(x, index)