from torch.autograd import Variable

As an AI language model, I am not able to execute code. However, I can provide you with information about the `torch.autograd.Variable` function in PyTorch.

`torch.autograd.Variable` is a class that holds a tensor and its gradient. It is a fundamental building block for performing automatic differentiation in PyTorch.

When a tensor is wrapped in a `Variable`, PyTorch keeps track of all the operations performed on it, allowing you to calculate gradients automatically using the chain rule.

For example, if you have a tensor `x` and you want to compute its gradient with respect to some other tensor `y`, you can create a `Variable` object for `x` and use it to compute the gradient:

import torch
x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
y = 2 * x + 1
z = y.mean()
z.backward()

In this example, `x` is a tensor that requires a gradient (i.e., we want to compute the derivative of `z` with respect to `x`). We create a `Variable` object for `x` by setting the `requires_grad` flag to `True`.

We then perform some operations on `x` to compute `y` and `z`. Finally, we call `backward()` on `z` to compute the gradients, which are stored in the `grad` attribute of `x`.

Note that in recent versions of PyTorch, the `Variable` class has been deprecated in favor of directly using tensors with the `requires_grad` flag. The above example can be rewritten as:

import torch
x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
y = 2 * x + 1
z = y.mean()
z.backward()