code: def test_score(nrows, ncols, nclusters, n_parts, input_type, client):
      from cuml.dask.cluster import KMeans as cumlKMeans
  
      from cuml.dask.datasets import make_blobs
  
      X, y = make_blobs(
          n_samples=int(nrows),
          n_features=ncols,
          centers=nclusters,
          n_parts=n_parts,
          cluster_std=0.01,
          shuffle=False,
          random_state=10,
      )
  
      if input_type == "dataframe":
          X_train = to_dask_cudf(X)
          y_train = to_dask_cudf(y)
          y = y_train
      elif input_type == "array":
          X_train, y_train = X, y
  
      cumlModel = cumlKMeans(
          init="k-means||", n_clusters=nclusters, random_state=10
      )
  
      cumlModel.fit(X_train)
  
      actual_score = cumlModel.score(X_train)
  
      local_model = cumlModel.get_combined_model()
      expected_score = local_model.score(X_train.compute())
  
      assert abs(actual_score - expected_score) < 9e-3
  
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 5 for calling a global function with example usage: test_score(nrows, ncols, nclusters, n_parts, input_type, client) without return types