code: def test_large_data_no_overflow(
      nrows_per_part, ncols, nclusters, client
  ):
      from cuml.dask.cluster import KMeans as cumlKMeans
      from cuml.dask.datasets import make_blobs
  
      n_parts = len(list(client.has_what().keys()))
  
      X, y = make_blobs(
          n_samples=nrows_per_part * n_parts,
          n_features=ncols,
          centers=nclusters,
          n_parts=n_parts,
          cluster_std=0.01,
          random_state=10,
      )
  
      X_train, y_train = X, y
  
      X.compute_chunk_sizes().persist()
  
      cumlModel = cumlKMeans(
          init="k-means||", n_clusters=nclusters, random_state=10
      )
  
      cumlModel.fit(X_train)
      n_predict = int(X_train.shape[0] / 4)
      cumlLabels = cumlModel.predict(
          X_train[:n_predict, :], delayed=False
      )
  
      cumlPred = cp.array(cumlLabels.compute())
      labels = cp.squeeze(y_train.compute()[:n_predict])
  
      print(str(cumlPred))
      print(str(labels))
  
      assert 1.0 == adjusted_rand_score(labels, cumlPred)
  
file_path: cuml/python/cuml/tests/dask/test_dask_kmeans.py
imports: from cuml.dask.common.dask_arr_utils import to_dask_cudf
  from sklearn.metrics import adjusted_rand_score
  from cuml.metrics import adjusted_rand_score
  import dask.array
  from cuml.testing.utils import stress_param
  from cuml.testing.utils import quality_param
  from cuml.testing.utils import unit_param
  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_large_data_no_overflow(nrows_per_part, ncols, nclusters, client) without return types