partial_fit example code does not work

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### Desribe the bug

running the example code from the partial_fit example in the docs throws an error.

thanks for the help!

Stack trace:
```
ValueError Traceback (most recent call last)
Cell In[10], line 21
19 # Incrementally fit the topic model by training on 1000 documents at a time
20 for index in range(0, len(docs), 1000):
---> 21 topic_model.partial_fit(docs[index: index+1000])

File ~/.venv/lib/python3.12/site-packages/bertopic/_bertopic.py:725, in BERTopic.partial_fit(self, documents, embeddings, y)
722 umap_embeddings = self._reduce_dimensionality(embeddings, y, partial_fit=True)
724 # Cluster reduced embeddings
--> 725 documents, self.probabilities_ = self._cluster_embeddings(umap_embeddings, documents, partial_fit=True)
726 topics = documents.Topic.to_list()
728 # Map and find new topics

File ~/.venv/lib/python3.12/site-packages/bertopic/_bertopic.py:3771, in BERTopic._cluster_embeddings(self, umap_embeddings, documents, partial_fit, y)
3769 logger.info("Cluster - Start clustering the reduced embeddings")
3770 if partial_fit:
-> 3771 self.hdbscan_model = self.hdbscan_model.partial_fit(umap_embeddings)
3772 labels = self.hdbscan_model.labels_
3773 documents["Topic"] = labels

File ~/.venv/lib/python3.12/site-packages/sklearn/base.py:1473, in _fit_context..decorator..wrapper(estimator, *args, **kwargs)
1466 estimator._validate_params()
1468 with config_context(
1469 skip_parameter_validation=(
1470 prefer_skip_nested_validation or global_skip_validation
1471 )
1472 ):
-> 1473 return fit_method(estimator, *args, **kwargs)

File ~/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:2277, in MiniBatchKMeans.partial_fit(self, X, y, sample_weight)
2274 self._n_since_last_reassign = 0
2276 with _get_threadpool_controller().limit(limits=1, user_api="blas"):
-> 2277 _mini_batch_step(
2278 X,
2279 sample_weight=sample_weight,
2280 centers=self.cluster_centers_,
2281 centers_new=self.cluster_centers_,
2282 weight_sums=self._counts,
2283 random_state=self._random_state,
2284 random_reassign=self._random_reassign(),
2285 reassignment_ratio=self.reassignment_ratio,
2286 verbose=self.verbose,
2287 n_threads=self._n_threads,
2288 )
2290 if self.compute_labels:
2291 self.labels_, self.inertia_ = _labels_inertia_threadpool_limit(
2292 X,
2293 sample_weight,
2294 self.cluster_centers_,
2295 n_threads=self._n_threads,
2296 )

File ~/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:1633, in _mini_batch_step(X, sample_weight, centers, centers_new, weight_sums, random_state, random_reassign, reassignment_ratio, verbose, n_threads)
1578 """Incremental update of the centers for the Minibatch K-Means algorithm.
1579
1580 Parameters
(...)
1628 the centers.
1629 """
1630 # Perform label assignment to nearest centers
1631 # For better efficiency, it's better to run _mini_batch_step in a
1632 # threadpool_limit context than using _labels_inertia_threadpool_limit here
-> 1633 labels, inertia = _labels_inertia(X, sample_weight, centers, n_threads=n_threads)
1635 # Update centers according to the labels
1636 if sp.issparse(X):

File ~/.venv/lib/python3.12/site-packages/sklearn/cluster/_kmeans.py:813, in _labels_inertia(X, sample_weight, centers, n_threads, return_inertia)
810 _labels = lloyd_iter_chunked_dense
811 _inertia = _inertia_dense
--> 813 _labels(
814 X,
815 sample_weight,
816 centers,
817 centers_new=None,
818 weight_in_clusters=None,
819 labels=labels,
820 center_shift=center_shift,
821 n_threads=n_threads,
822 update_centers=False,
823 )
825 if return_inertia:
826 inertia = _inertia(X, sample_weight, centers, labels, n_threads)

File _k_means_lloyd.pyx:26, in sklearn.cluster._k_means_lloyd.lloyd_iter_chunked_dense()

ValueError: Buffer dtype mismatch, expected 'const double' but got 'float'
```

### Reproduction

copy and pasted from example code [here](https://maartengr.github.io/BERTopic/api/bertopic.html#bertopic._bertopic.BERTopic.partial_fit)

I'm on an M3 MacBook Pro
Python 3.12.4
scikit-learn 1.5.1
bertopic 0.16.3
numpy 1.26.4
scipy 1.14.0

```python
from sklearn.datasets import fetch_20newsgroups
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import IncrementalPCA
from bertopic.vectorizers import OnlineCountVectorizer
from bertopic import BERTopic

# Prepare documents
docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))["data"]

# Prepare sub-models that support online learning
umap_model = IncrementalPCA(n_components=5)
cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)
vectorizer_model = OnlineCountVectorizer(stop_words="english", decay=.01)

topic_model = BERTopic(umap_model=umap_model,
hdbscan_model=cluster_model,
vectorizer_model=vectorizer_model)

# Incrementally fit the topic model by training on 1000 documents at a time
for index in range(0, len(docs), 1000):
topic_model.partial_fit(docs[index: index+1000])
```

### BERTopic Version

0.16.3

Start with the partial_fit example linked in the issue and reproduce it using the listed BERTopic, Python, scikit-learn, NumPy, and SciPy versions. Trace BERTopic.partial_fit through _cluster_embeddings to the MiniBatchKMeans call shown in the stack trace. Done means the documented loop completes without the buffer dtype mismatch.

Written by the indexing model from the issue text.