Lightweight soft topic assignment via temperature-scaled embedding distances

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### Feature request

Add a `soft_clustering_temp` parameter to `transform()` that computes soft topic distributions using temperature-scaled softmax on embedding distances to topic centroids:

```python
# Hard assignment (current default)
topics, probs = topic_model.transform(new_docs)

# Soft assignment with temperature scaling
topics, probs = topic_model.transform(new_docs, soft_clustering_temp=0.5)
# probs is now a (n_docs, n_topics) matrix of soft assignments
```

### Motivation

Getting soft (probabilistic) topic assignments currently requires either:

1. `calculate_probabilities=True` — expensive for large datasets and HDBSCAN-specific
2. `approximate_distribution()` — good but requires re-running the vectorizer

Users frequently ask about soft/overlapping topic assignments:
- [#1419](https://github.com/MaartenGr/BERTopic/issues/1419) — non-mutually exclusive topics
- [#2017](https://github.com/MaartenGr/BERTopic/issues/2017) — probability inconsistency
- [#1613](https://github.com/MaartenGr/BERTopic/issues/1613) — negative probabilities
- [#1808](https://github.com/MaartenGr/BERTopic/issues/1808) — probabilities NoneType
- [#1962](https://github.com/MaartenGr/BERTopic/issues/1962) — probabilities empty with zero-shot

The temperature-scaled approach is clustering-agnostic (works with any clustering model, not just HDBSCAN), lightweight (no vectorizer, no refit), and provides a smooth probability distribution over all topics.

### Your contribution

I can submit a PR that adds `soft_clustering_temp` to `transform()`. Low temperature → sharper (near-hard) assignments; high temperature → softer distributions. Default is `None` — existing behavior unchanged.

I've already been prototyping this in my fork (working implementation with tests). Since this adds a new parameter, I'd value your steer on the API (name, whether it lives on `transform()` or a dedicated method) before I open the PR.

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The requested entry point is transform(); start by locating its implementation and existing tests, then review how current topic assignments and probabilities are produced. Confirm the API and temperature behavior with maintainers before implementing; done means the default remains unchanged and the new option returns tested soft assignments across supported clustering models.

Written by the indexing model from the issue text.