# What is PyTorch .detach() method?

What is PyTorch `.detach()` method?

PyTorch's detach method works on the tensor class. 

`tensor.detach()` creates a tensor that shares storage with tensor that does not require gradient. `tensor.clone()` creates a copy of tensor that imitates the original tensor's `requires_grad` field.

You should use `detach()` when attempting to remove a tensor from a computation graph, and clone as a way to copy the tensor while still keeping the copy as a part of the computation graph it came from.

Let's see that in an example here

```python
X = torch.ones((28, 28), dtype=torch.float32, requires_grad=True)
y = X**2
z = X**2

result = (y+z).sum()

torchviz.make_dot(result).render('Attached', format='png')
```

![1.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1603379723625/NmP78Zuam.png)

And now one with the detach.
```python
X = torch.ones((28, 28), dtype=torch.float32, requires_grad=True)
y = X**2
z = X.detach()**2

result = (y+z).sum()

torchviz.make_dot(result).render('Attached', format='png')
```

![Screen Shot 2020-10-22 at 8.48.43 PM.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1603379927351/ZsGdLEa2U.png)

As you can see now that the branch of computation with `x**2` is no longer tracked. This is reflected in the gradient of the result which no longer records the contribution of this branch