Hi,

I need to perform a normalization [0,1] over each channel of a tensor [shape(BxCxWxH)] as a part of the model and I wrote this code:

```
def normalize_channels(_x, every=True):
out = torch.ones_like(_x)
for i in range(_x.shape[0]):
if every:
for j in range(_x[i].shape[0]):
min_ = _x[i][j].min()
max_ = _x[i][j].max()
out[i][j].copy_((_x[i][j] - min_) * (1 / (max_ - min_)))
else:
min_ = _x[i].min()
max_ = _x[i].max()
out.copy_((_x[i] - min_) * (1 / (max_ - min_)))
return out
```

but the computational time increases too much. Someone has any ideas on how to improve the performance and how to remove the indexing?