code: def test_default_n_neighbors(client):
      n_samples = 50
      n_feats = 50
      k = 15
  
      from cuml.dask.neighbors import NearestNeighbors as daskNN
      from cuml.neighbors.nearest_neighbors_mg import (
          NearestNeighborsMG as cumlNN,
      )
  
      from sklearn.datasets import make_blobs
  
      n_samples = _scale_rows(client, n_samples)
  
      X, y = make_blobs(
          n_samples=n_samples, n_features=n_feats, random_state=0
      )
  
      X = X.astype(np.float32)
  
      X_cudf = _prep_training_data(client, X, 1)
  
      cumlModel = daskNN(streams_per_handle=5)
      cumlModel.fit(X_cudf)
  
      ret = cumlModel.kneighbors(X_cudf, return_distance=False)
  
      assert ret.shape[1] == cumlNN().n_neighbors
  
      cumlModel = daskNN(n_neighbors=k)
      cumlModel.fit(X_cudf)
  
      ret = cumlModel.kneighbors(X_cudf, k, return_distance=False)
  
      assert ret.shape[1] == k
  
file_path: cuml/python/cuml/tests/dask/test_dask_nearest_neighbors.py
imports: import platform
  from cuml.testing.utils import array_equal
  from sklearn.neighbors import KNeighborsClassifier
  from cuml.testing.utils import unit_param, quality_param, stress_param
  from cuml.dask.common import utils
  from cuml.common import has_scipy
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 7 for calling a global function with example usage: test_default_n_neighbors(client) without return types