Systematic test units for fit_transform()

Nobody has claimed this yet.

### Feature request

As identified in PR #2191, the current test units do not cover the _process_ of fitting a model. In other words, is not testing the implementation of `fit_transform()`. Consequently, different current, and future, features that are performed at the `fit_transform()` level are not tested in any systematic way. We realized about this when [debugging topic reduction by declaring a `nr_topics` for the model before fitting](https://github.com/MaartenGr/BERTopic/pull/2191). However, this issue might involve all the core features, and most of the optional ones.

Currently, in [conftest.py](https://github.com/MaartenGr/BERTopic/blob/master/tests/conftest.py) the tests define the models and `fit`them for further testing in the other units.
https://github.com/MaartenGr/BERTopic/blob/c3ec85dec8eeac704b30812dfed4ac8cd7d13561/tests/conftest.py#L50C1-L55C17

As such, some improvement is required for the tests to cover for the `fit_transform()` method, the core of the library.

### Motivation

This is required to systematically test the internal consistency of all features and the overall work pipeline.

### Your contribution

I can't tackle this issue yet due to time availability, since I will need to familiarize myself more with the pytest framework first. I will come back in a future to tackle this, but I leave the issue open as a reminder, and in case someone else is up for the challenge.

Start with tests/conftest.py, especially the model setup around lines 50-55, and review PR #2191 and the topic-reduction case involving nr_topics before fitting. Inspect the existing test units and extend coverage so the fit_transform() process is tested systematically across the core and relevant optional features.

Written by the indexing model from the issue text.