code: def test_kneighborsRegressor_multioutput_uniform_weight():
      # Test k-neighbors in multi-output regression with uniform weight
      rng = check_random_state(0)
      n_features = 5
      n_samples = 40
      n_output = 4
  
      X = rng.rand(n_samples, n_features)
      y = rng.rand(n_samples, n_output)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=0
      )
      knn = cuKNN()
      knn.fit(X_train, y_train)
  
      neigh_idx = knn.kneighbors(X_test, return_distance=False).astype(
          np.int32
      )
  
      y_pred_idx = np.array(
          [np.mean(y_train[idx], axis=0) for idx in neigh_idx]
      )
  
      y_pred = knn.predict(X_test)
  
      assert y_pred.shape[0] == y_test.shape[0]
      assert y_pred_idx.shape == y_test.shape
      assert_array_almost_equal(y_pred, y_pred_idx)
  
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 2 for calling a global function with example usage: test_kneighborsRegressor_multioutput_uniform_weight() without return types