```
import numpy
import torch
wine_path = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
wineq_numpy = numpy.loadtxt(wine_path, dtype=numpy.float32, delimiter=";",skiprows=1)
wineq=torch.from_numpy(wineq_numpy)
target=wineq[:,-1]
target = target.unsqueeze(-1)
print(target.shape)
print(target_onehot.shape)
target_onehot = torch.FloatTensor(target.shape[0], 10)
target_onehot.zero_()
target_onehot.scatter_(1, target, 1)
```

Gives error

```
RuntimeError Traceback (most recent call last)
<ipython-input-192-2622c1acffeb> in <module>()
1 target_onehot = torch.FloatTensor(target.shape[0], 10)
2 target_onehot.zero_()
----> 3 target_onehot.scatter_(1, target, 1)
RuntimeError: index 4668431376536567808 is out of bounds for dimension 1 with size 10
```

I donâ€™t get it why its throwing this error ?

I reckon this is the way ?

i tried in doing by generating random values it works.

```
import torch
batch_size = 5
nb_digits = 10
# Dummy input that HAS to be 2D for the scatter (you can use view(-1,1) if needed)
y = torch.LongTensor(batch_size,1).random_() % nb_digits
# One hot encoding buffer that you create out of the loop and just keep reusing
y_onehot = torch.FloatTensor(batch_size, nb_digits)
# In your for loop
y_onehot.zero_()
y_onehot.scatter_(1, y, 1)
print(y)
```