code: def test_aligned_update_params(aligned_iris, aligned_iris_relations):
      data, target = aligned_iris
      n_neighbors = [15, 15, 15, 15, 15]
      small_aligned_model = AlignedUMAP(n_neighbors=n_neighbors[:3])
      small_aligned_model.fit(
          data[:3], relations=aligned_iris_relations[:2]
      )
      small_aligned_model.update(
          data[3],
          relations=aligned_iris_relations[2],
          n_neighbors=n_neighbors[3],
      )
      for i, slice in enumerate(data[:4]):
          data_dmat = pairwise_distances(slice)
          true_nn = np.argsort(data_dmat, axis=1)[:, :10]
          embd_dmat = pairwise_distances(
              small_aligned_model.embeddings_[i]
          )
          embd_nn = np.argsort(embd_dmat, axis=1)[:, :10]
          assert nn_accuracy(true_nn, embd_nn) >= 0.45
  
file_path: umap/umap/tests/test_aligned_umap.py
imports: import pytest
  from umap import AlignedUMAP
  from sklearn.metrics import pairwise_distances
  from sklearn.cluster import KMeans
  import numpy
  from sklearn.metrics import adjusted_rand_score
label_desc: Use image node_id 5 for calling a global function with example usage: test_aligned_update_params(aligned_iris, aligned_iris_relations) without return types