def METHOD_NAME(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