code: def test_neighborhood_predictions(
      nrows, ncols, n_neighbors, n_clusters, datatype, algo
  ):
      if not has_scipy():
          pytest.skip(
              "Skipping test_neighborhood_predictions because "
              + "Scipy is missing"
          )
  
      X, y = make_blobs(
          n_samples=nrows,
          centers=n_clusters,
          n_features=ncols,
          random_state=0,
      )
  
      if datatype == "dataframe":
          X = cudf.DataFrame(X)
  
      knn_cu = cuKNN(algorithm=algo)
      knn_cu.fit(X)
      neigh_ind = knn_cu.kneighbors(
          X, n_neighbors=n_neighbors, return_distance=False
      )
      del knn_cu
      gc.collect()
  
      if datatype == "dataframe":
          assert isinstance(neigh_ind, cudf.DataFrame)
          neigh_ind = neigh_ind.to_numpy()
      else:
          assert isinstance(neigh_ind, cp.ndarray)
  
      labels, probs = predict(neigh_ind, y, n_neighbors)
  
      assert array_equal(labels, y)
  
imports: import gc
  from cuml.common import has_scipy
  import cuml
  import sklearn
  from cuml.internals.safe_imports import cpu_only_import_from
  from numpy.testing import assert_array_equal, assert_allclose
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  import math
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.neighbors import NearestNeighbors
  from sklearn.neighbors import NearestNeighbors
  from cuml.datasets import make_blobs
  from sklearn.metrics import pairwise_distances
  from cuml.metrics import pairwise_distances
  from cuml.internals import logger
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 6 for calling a global function with example usage: test_neighborhood_predictions(nrows, ncols, n_neighbors, n_clusters, datatype, algo) without return types