The output in the forward pass will be zero, but the gradient would be NaN:
x = torch.tensor(1.0, requires_grad=True)
out = torch.pow((1 - x), 0.75)
print(out)
> tensor(0., grad_fn=<PowBackward0>)

out.backward()
print(x.grad)
> tensor(nan)