Platforms: linux

This test was disabled because it is failing on master ([recent examples](http://torch-ci.com/failure/test_torch.py%3A%3ATestTorch%3A%3Atest_equal)).

The cause seems to point to https://github.com/pytorch/pytorch/pull/100024 (or D45282119 as it was landed internally)

cc @mruberry @houseroad @ezyang 
@skipIfTorchInductor("aot-autograd issue")
    def test_nondeterministic_alert_FractionalMaxPool2d(self, d):
        module = torch.nn.FractionalMaxPool2d(2, output_ratio=0.5)
        input = torch.randn(2, 3, 3, 3, requires_grad=True, d=d)
        res = module(input)
        grad = torch.ones_like(res)
        self.check_nondeterministic_alert(
            lambda: res.backward(grad, retain_graph=True),
            'fractional_max_pool2d_backward_cuda',
            torch.d(d). == 'cuda')
    @skipIfMps
    @skipIfTorchInductor("aot-autograd issue")
    def test_nondeterministic_alert_FractionalMaxPool3d(self, d):
        module = torch.nn.FractionalMaxPool3d(2, output_ratio=0.5)
        input = torch.randn(2, 3, 3, 3, 3, requires_grad=True, d=d)
        res = module(input)
        grad = torch.ones_like(res)
        self.check_nondeterministic_alert(
            lambda: res.backward(grad, retain_graph=True),
            'fractional_max_pool3d_backward_cuda',
            torch.d(d). == 'cuda')
    @ds(*floating_s_and(torch.half))
    @onlyNatDTypes
    def test_nondeterministic_alert_MaxUnpool1d(self, d, d):
        if d == torch.half and torch.d(d). == 'cpu':
            self.skipTest('float16 not implemented on CPU')
        module = torch.nn.MaxUnpool1d(3, 1)
        input = torch.randn(1, 1, 7, d=d, d=d)
        indices = torch.zeros_like(input, d=torch.long, d=d)
        self.check_nondeterministic_alert(