I have simple example (1.1.0 pytorch)

```
import torch
print(torch.__version__)
x = torch.rand(1, 3, 2, 2)
for running_stats in [False, True]:
for eval in [False, True]:
print('running_stats:', running_stats, 'eval:', eval)
bn = torch.nn.BatchNorm2d(3)
bn.track_running_stats = running_stats
if eval:
bn.eval()
print('\t', bn.running_mean)
y1 = bn(x)
print('\t', bn.running_mean)
y2 = bn(x)
print('\t', bn.running_mean)
print('\t', (y1 - y2).abs().sum())
```

I expecting here that setting bn.eval() is enough for BN to return same results and keep same running mean and var. I also expect that different running mean and var will produce different results.

Output:

```
1.1.0
running_stats: False eval: False
tensor([0., 0., 0.])
tensor([0.0529, 0.0558, 0.0643])
tensor([0.1005, 0.1061, 0.1222])
tensor(0., grad_fn=<SumBackward0>)
running_stats: False eval: True
tensor([0., 0., 0.])
tensor([0.0529, 0.0558, 0.0643])
tensor([0.1005, 0.1061, 0.1222])
tensor(0., grad_fn=<SumBackward0>)
running_stats: True eval: False
tensor([0., 0., 0.])
tensor([0.0529, 0.0558, 0.0643])
tensor([0.1005, 0.1061, 0.1222])
tensor(0., grad_fn=<SumBackward0>)
running_stats: True eval: True
tensor([0., 0., 0.])
tensor([0., 0., 0.])
tensor([0., 0., 0.])
tensor(0., grad_fn=<SumBackward0>)
```

Issues:

- Running mean and var seems to have ABSOLUTELY no effect on output in this example
- Setting bn.eval() is not enough to keep same running mean and var