How to validate the Dataset.mapping

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Using the same example from https://making.lyst.com/lightfm/docs/examples/dataset.html#building-the-id-mappings

How could I validade the mapping of each item feature?

When I call ```user_id_map, u_f_map, item_id_map, i_f_map = dataset.mapping()```

after this
```item_features = dataset.build_item_features(((x['ISBN'], [x['Book-Author']]) for x in get_book_features()))```

I got this result from item_id_map
```
{'034545104X': 0,
'0155061224': 1,
'0446520802': 2,
'052165615X': 3...}
```
Now, I know that '034545104X' is mapped as 0. Looking at item_features built before I got this

```
(0, 0) 0.5
(0, 343789) 0.5
(1, 1) 0.5
(1, 428522) 0.5
(2, 2) 0.5
(2, 341954) 0.5
(3, 3) 0.5 .......
```

Looking at ```book_features``` as a pandas dataframe

As seen, getting the item mapped as 0 which is the item 034545104X. And filtering the dataframe, for this item, the book-author is `Flesh Tones: A Novel`

But When I do this
```
item_feature_inverse_map = {v:k for k, v in i_f_map.items()}
print(item_feature_inverse_map[343789])
```

The result is `'M. J. Rose'` which is different of `Flesh Tones: A Novel`.

Start with the Dataset.mapping() and build_item_features() entry points, using the linked dataset example as the initial reference. Trace how item IDs and item-feature IDs are assigned, then document or clarify the expected inverse mapping behavior and validate it against the reported ISBN and author values.

Written by the indexing model from the issue text.