This rule raises when a `torch.autograd.Variable` is instantiated.

== Why is this an issue?

The Pytorch Variable API has been deprecated. The behavior of Variables is now provided by the Pytorch tensors and can be controlled with the `requires_grad` parameter. 

The Variable API now returns tensors anyway, so there should not be any breaking changes.

== How to fix it

Replace the call to `torch.autograd.Variable` with a call to `torch.tensor` and set the `requires_grad` attribute to `True` if needed.

=== Code examples

==== Noncompliant code example

[source,python,diff-id=1,diff-type=noncompliant]
----
import torch

x = torch.autograd.Variable(torch.tensor([1.0]), requires_grad=True) # Noncompliant
x2 = torch.autograd.Variable(torch.tensor([1.0])) # Noncompliant
----

==== Compliant solution

[source,python,diff-id=1,diff-type=compliant]
----
import torch

x = torch.tensor([1.0], requires_grad=True)
x2 = torch.tensor([1.0])
----


== Resources
=== Documentation

* Pytorch documentation - https://pytorch.org/docs/stable/autograd.html#variable-deprecated[Variable API]


ifdef::env-github,rspecator-view[]

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== Implementation specification 

Should be pretty straighforward to implement.

=== Message 

Primary : Replace this call with a call to "torch.tensor".


=== Issue location

Primary : Name of the function call

=== Quickfix

Might be tricky to know how to call the `torch.tensor` function.
If there is an import like `from torch import tensor`, then replace with `tensor(...)`
If not, then replace with `torch.tensor(...)`


endif::env-github,rspecator-view[]