Missing sorting of features?

I was looking at `reagent/preprocessing/preprocessor.py`, it seems like the `Preprocessor` expects that the `input` has already been sorted according to the normalization parameters, but I believe that's not actually the case. Instead, the `input` is just in increasing feature idx.

One of the first lines of the forward pass of the `Preprocessor` is:

`split_input = torch.split(input, self.split_sections, dim=1)`

Which appears to expect that the input tensor has been sorted as in `sorted_features`.

The input to the preprocessor is generated by `reagent/workflow/data_fetcher.py`. Inside that file, the order is generated by:

```
def infer_states_names(df, multi_steps: Optional[int]):
""" Infer possible state names from states and next state features. """
state_keys = get_distinct_keys(df, "state_features")
next_states_is_col_arr_map = not (multi_steps is None)
next_state_keys = get_distinct_keys(
df, "next_state_features", is_col_arr_map=next_states_is_col_arr_map
)
return sorted(set(state_keys) | set(next_state_keys))
```

This later is passed to `make_sparse2dense(df, col_name: str, possible_keys: List)` as `possible_keys` and used to generate the dense feature array input.

I believe either the preprocessor needs to first re-arrange the input to match the sorted feature ordering, or the sorted ordering needs to be used when generating the datasets as the `possible_keys` variable.