No, you would detach the tensor (in case it has an Autograd history), push the data to the CPU, and transform it to a numpy array via:
preds = torch.randn(10, 10, requires_grad=True) # GPUTensors with Autograd history
preds_arr = preds.detach().cpu().numpy()
np_fun(preds_arr)