Adding a dimension to a tensor can be important when you’re building
deep learning models. In NumPy, you can do this by inserting `None`

into
the axis you want to add:

```
import numpy as np
x1 = np.zeros((10, 10))
x2 = x1[None, :, :]
```

```
>>> print(x2.shape)
(1, 10, 10)
```

**EDIT:** Good news! As of version 0.1.10, PyTorch supports `None`

-style indexing. You should probably use that. But if you prefer to do it the old-fashioned way, read on.

Fortunately, it’s easy enough in PyTorch. Just pass the axis index into
the `.unsqueeze()`

method.

```
import torch
x1 = torch.zeros(10, 10)
x2 = x1.unsqueeze(0)
```

```
>>> print(x2.size())
torch.Size([1, 10, 10])
```

You can also do it in place using the underscore version
`.unsqueeze_()`

.

```
x1 = torch.zeros(10, 10)
x1.unsqueeze_(0)
```

```
>>> print(x1.size())
torch.Size([1, 10, 10])
```

Want to know more cool tensor tricks? Check out my post on pairwise distance!