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 
sample_idx = sample_indices[i, j]
                        if zero_prob_idx >= 0:
                            self.assertNotEqual(sample_idx, zero_prob_idx,
                                                msg="sampled an index with zero probability")
                        self.assertNotIn(sample_idx, row_samples, "sampled an index twice")
                        row_samples[sample_idx] = True
            
            n_col = 4
            prob_dist = make_prob_dist([n_col], is_contiguous).fill_(1)
            zero_prob_idx = 1  
            prob_dist[zero_prob_idx] = 0
            n_sample = 20
            sample_indices = torch.multinomial(prob_dist, n_sample, True)
            for sample_index in sample_indices:
                self.assertNotEqual(sample_index, zero_prob_idx, msg="sampled an index with zero probability")
            s_dim = sample_indices.dim()
            self.assertEqual(sample_indices.dim(), 1, msg="wrong number of dimensions")
            self.assertEqual(prob_dist.dim(), 1, msg="wrong number of prob_dist dimensions")
            self.assertEqual(sample_indices.size(0), n_sample, msg="wrong number of samples")
        
        n_row, n_col = 2, 3
        prob_dist = make_prob_dist([n_row, n_col], True)
        n_sample = 1
        sample_indices = torch.multinomial(prob_dist, n_sample, True)
        self.assertEqual(sample_indices.dim(), 2, msg="wrong number of dimensions")
        self.assertEqual(sample_indices.size(1), n_sample, msg="wrong number of samples")
    
    @onlyCUDA
    @ds(torch.float, torch.double, torch.half)
    def test_multinomial_deterministic(self, d, d):