Attempting to do batch inference using my trained LM for the purpose of getting the document hidden states (e.g. the `outputs`

and `raw_outputs`

produced by the `LinearDecoder`

)

I just noticed that when specifying a `test`

ItemList for `load_learner`

, the sampler for all dataloaders is a `SequentialSampler`

… whereas I was expected train to use `SortishSampler`

and both the validation and test to use `SortSampler`

.

```
inf_items = TextList.from_df(inf_df, path=LM_PATH, cols=corpus_cols)
inf_learn = load_learner(LM_PATH, f'{pre}export_lm.pkl', test=inf_items)
inf_learn.model = inf_learn.model.to(device)
inf_learn.model = inf_learn.model.eval()
inf_learn.data.train_dl.sampler, inf_learn.data.valid_dl.sampler, inf_learn.data.test_dl.sampler
# (<torch.utils.data.sampler.SequentialSampler at 0x7f1e1f6b2240>,
# <torch.utils.data.sampler.SequentialSampler at 0x7f1e1f6b22e8>,
# <torch.utils.data.sampler.SequentialSampler at 0x7f1e1f6b2438>)
```

This seems also problematic in `<learner>.get_preds`

because of this line:

```
if ordered and hasattr(self.dl(ds_type), 'sampler'):
```

… it will return `True`

in this case even though the examples are already in sequential order, resulting in the unnecessary ordering of what is already ordered.