Adding precomputed distances for Parametric UMAP

Nobody has claimed this yet.

I'm trying to figure out how we could enable precomputed distances for Parametric UMAP, since fit_transform only takes in `X` as either the distance matrix, _or_ the data, but Parametric UMAP would need both the distance matrix _and_ the data as input.

One option that wouldn't require modification to anything but parametric_umap.py would be to add a fit_transform method that takes in precomputed distances, and grabs the data as self._X:

```python
def fit_transform(self, X, y=None, precomputed_distances=None):

if self.metric == "precomputed":
if precomputed_distances is None:
raise ValueError(
"Precomputed distances must be supplied if metric \
is precomputed."
)
# prepare X for traning the network
self._X = X
# geneate the graph on precomputed distances
return super().fit_transform(precomputed_distances, y)
else:
return super().fit_transform(X, y)
```

then, in _fit_embed_data, grab back X.

```python
def _fit_embed_data(self, X, n_epochs, init, random_state):

if self.metric == "precomputed":
X = self._X
...

```

Does that seem reasonable? I can make a PR if so.

Start in parametric_umap.py at fit_transform and _fit_embed_data, where the issue proposes passing both training data and precomputed distances. Trace how Parametric UMAP currently prepares X and delegates to the parent implementation, then determine how both inputs should be retained without changing non-precomputed behavior. Done means Parametric UMAP accepts precomputed distances while still training on the original data.

Written by the indexing model from the issue text.