#246 Fix issue where training was not kept consistent

[Dref360]

Summary:

Fixes #246

Features:

import torch
from transformers import BertForSequenceClassification
from baal.bayesian.dropout import patch_module,unpatch_module

pretrained_weights = 'bert-base-uncased'
use_cuda = torch.cuda.is_available()

model = BertForSequenceClassification.from_pretrained(pretrained_model_name_or_path=pretrained_weights)
print(f"Droputs enabled: {model.dropout.training}, model= {model.training}") # False here

>>> Droputs enabled: False, model= False

model = patch_module(model,inplace=False)
print(f"Droputs enabled: {model.dropout.training}, model= {model.training}") # True here

>>> Droputs enabled: False, model= False

model = unpatch_module(model,inplace=False)
print(f"Droputs enabled: {model.dropout.training}, model= {model.training}") # Should be False here but this is true??

>>> Droputs enabled: False, model= False

Checklist:

• [x] Your code is documented (To validate this, add your module to tests/documentation_test.py).
• [x] Your code is tested with unit tests.
• [x] You moved your Issue to the PR state.