AttributeError: Can't get attribute 'EuclideanDistance64' on <module 'sklearn.metrics._dist_metrics'

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When I load the generated bertopic model, it give the following error traces:
```
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
@numba.jit()
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
@numba.jit()
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
@numba.jit()
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
@numba.jit()
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:557: RuntimeWarning: invalid value encountered in cast
arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:557: RuntimeWarning: invalid value encountered in cast
arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:557: RuntimeWarning: invalid value encountered in cast
arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:557: RuntimeWarning: invalid value encountered in cast
arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:557: RuntimeWarning: invalid value encountered in cast
arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:557: RuntimeWarning: invalid value encountered in cast
arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
Traceback (most recent call last):
File "/home/21zz42/Asset-Management-Topic-Modeling/Code/RQ1/best_model.py", line 24, in
topic_model = BERTopic.load(os.path.join(path_model, model_name))
File "/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/bertopic/_bertopic.py", line 2998, in load
topic_model = joblib.load(file)
File "/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/joblib/numpy_pickle.py", line 648, in load
obj = _unpickle(fobj)
File "/home/21zz42/Asset-Management-Topic-Modeling/.venv/lib/python3.10/site-packages/joblib/numpy_pickle.py", line 577, in _unpickle
obj = unpickler.load()
File "/usr/lib/python3.10/pickle.py", line 1213, in load
dispatch[key[0]](self)
File "/usr/lib/python3.10/pickle.py", line 1538, in load_stack_global
self.append(self.find_class(module, name))
File "/usr/lib/python3.10/pickle.py", line 1582, in find_class
return _getattribute(sys.modules[module], name)[0]
File "/usr/lib/python3.10/pickle.py", line 331, in _getattribute
raise AttributeError("Can't get attribute {!r} on {!r}"
AttributeError: Can't get attribute 'EuclideanDistance64' on
```
When I am running the following code:
```
import os
import pickle
import pandas as pd

from bertopic import BERTopic

path_rq1 = os.path.join('Result', 'RQ1')
path_model = os.path.join(path_rq1, 'Model')

model_name = 'Challenge_preprocessed_gpt_summary_fzqzh0m6'
column = '_'.join(model_name.split('_')[:-1])

df = pd.read_json(os.path.join('Dataset', 'preprocessed.json'))
df['Challenge_topic'] = -1

indice = []
docs = []

for index, row in df.iterrows():
if pd.notna(row[column]) and len(row[column]):
indice.append(index)
docs.append(row[column])

topic_model = BERTopic.load(os.path.join(path_model, model_name))
topic_number = topic_model.get_topic_info().shape[0] - 1
topics, probs = topic_model.transform(docs)

# persist the topic terms
with open(os.path.join(path_rq1, 'Topic terms.pickle'), 'wb') as handle:
topic_terms = []
for i in range(topic_number):
topic_terms.append(topic_model.get_topic(i))
pickle.dump(topic_terms, handle, protocol=pickle.HIGHEST_PROTOCOL)

fig = topic_model.visualize_topics()
fig.write_html(os.path.join(path_rq1, 'Topic visualization.html'))

fig = topic_model.visualize_barchart(top_n_topics=topic_number, n_words=10)
fig.write_html(os.path.join(path_rq1, 'Term visualization.html'))

fig = topic_model.visualize_heatmap()
fig.write_html(os.path.join(path_rq1, 'Topic similarity visualization.html'))

# This uses the soft-clustering as performed by HDBSCAN to find the best matching topic for each outlier document.
topics_new = topic_model.reduce_outliers(docs, topics, probabilities=probs, strategy="probabilities")

# persist the document topics
for index, topic in zip(indice, topics_new):
df.at[index, 'Challenge_topic'] = topic

df = df[df.columns.drop(list(df.filter(regex=r'preprocessed|gpt_summary')))]
df.to_json(os.path.join(path_rq1, 'topics.json'), indent=4, orient='records')
```

Start with Code/RQ1/best_model.py at the BERTopic.load call and reproduce the failure while loading the generated model. Trace the joblib unpickling error involving sklearn.metrics._dist_metrics and determine the compatible loading conditions. Done means the saved model loads successfully and the subsequent transform call can run.

Written by the indexing model from the issue text.