code: def test_score(nrows, ncols, n_neighbors, n_clusters, datatype):
      # Using make_blobs here to check averages and neighborhoods
      X, y = make_blobs(
          n_samples=nrows,
          centers=n_clusters,
          cluster_std=0.01,
          n_features=ncols,
          random_state=0,
      )
  
      X = X.astype(np.float32)
      y = y.astype(np.float32)
  
      if datatype == "dataframe":
          X = cudf.DataFrame(X)
          y = cudf.DataFrame(y.reshape(nrows, 1))
  
      knn_cu = cuKNN(n_neighbors=n_neighbors)
      knn_cu.fit(X, y)
  
      assert knn_cu.score(X, y) >= 0.9999
  
file_path: cuml/python/cuml/tests/test_kneighbors_regressor.py
imports: from cuml.testing.utils import array_equal
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.internals.safe_imports import cpu_only_import_from
  from sklearn.model_selection import train_test_split
  from sklearn.utils.validation import check_random_state
  from sklearn.datasets import make_blobs
  from cuml.neighbors import KNeighborsRegressor
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 3 for calling a global function with example usage: test_score(nrows, ncols, n_neighbors, n_clusters, datatype) without return types