Summary:
There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports:
```2to3 -f future -w caffe2```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033
Reviewed By: seemethere
Differential Revision: D23808648
Pulled By: bugra
fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
Summary:
`SamplingTrain` layer is a wrapper around another layer subclassing `SamplingTrainableMixin`. When initiated in the training context, `SamplingTrain` produces sparse output of the wrapped layer. Output can be paired with `indices` to create Map schema. When initiated in prediction context, the full output of the wrap layer is produced.
This is liked the SampledFC function in model helper, https://fburl.com/gi9g1awh, with the ability to initiated in both trainig and prediction context.
I'd like to get consensus whether we should introduce the `SamplingTrain` layer and the accompaying mixin. This can probably be accomplished in some other way, but I think this is not too bad.
Reviewed By: xianjiec
Differential Revision: D4689887
fbshipit-source-id: 7be8a52d82f3a09a053378146262df1047ab26a8
