I am trying to normalize my targets (here landmark values) but to_tensor is not forcing the values to be between 0 and 1. How should that be enforced?

Also, am I looking for only one value for mean and one value for std, right? Each image has 4 landmarks and each landmark has x and y coordinates.

```
to_tensor = transforms.ToTensor()
landmarks_arr = []
for i in range(len(train_dataset)):
landmarks = to_tensor(train_dataset[i]['landmarks'])
landmarks[landmarks != landmarks] = 0 # this will set all nan to zero
landmarks_arr.append(landmarks)
mean = torch.mean(torch.stack(landmarks_arr, dim=0))#, dim=(0, 2, 3))
std = torch.std(torch.stack(landmarks_arr, dim=0)) #, dim=(0, 2, 3))
print(mean.shape)
print("mean is {} and std is {}".format(mean, std))
```

result is:

```
torch.Size([])
mean is 367.7143527453419 and std is 160.8974583202625
```

This is how landmarks are:

```
landmarks_arr[0]
tensor([[[502.2869, 240.4949],
[688.0000, 293.0000],
[346.0000, 317.0000],
[560.8283, 322.6830]]], dtype=torch.float64)
```

On a side note, is this line even correct?

```
mean = torch.mean(torch.stack(landmarks_arr, dim=0))#, dim=(0, 2, 3))
```