AlignedUMAP ignores n_components parameter

Hi,
AlignedUMAP produces a sequence of 2-dimensional embeddings regardless of the n_components' parameter value.
Here's some code taken from this page to demonstrate this.

import numpy as np
import sklearn.datasets
import umap
import umap.plot
import umap.utils as utils
import umap.aligned_umap

digits = sklearn.datasets.load_digits()
ordered_digits = digits.data[np.argsort(digits.data.sum(axis=1))]
ordered_target = digits.target[np.argsort(digits.data.sum(axis=1))]
slices = [ordered_digits[150 * i:min(ordered_digits.shape[0], 150 * i + 400)] for i in range(10)]
relation_dict = {i+150:i for i in range(400-150)}
relation_dicts = [relation_dict.copy() for i in range(len(slices) - 1)]

aligned_mapper_3D = umap.AlignedUMAP(n_components=3).fit(slices, relations=relation_dicts)
aligned_mapper_3D.embeddings_[0].shape

Reproduce the example from aligned_umap_basic_usage.html with umap.AlignedUMAP(n_components=3), then inspect the umap.AlignedUMAP entry point and how embeddings_ is produced. Done means embeddings_[0] and the other aligned embeddings use the requested component count rather than always having two dimensions.