code: def test_all_kmeans_params(
      n_clusters,
      max_iter,
      init,
      oversampling_factor,
      max_samples_per_batch,
      random_state,
  ):
      np.random.seed(0)
      X = np.random.rand(1000, 10)
  
      if init == "preset":
          init = np.random.rand(n_clusters, 10)
  
      cuml_kmeans = cuml.KMeans(
          n_clusters=n_clusters,
          max_iter=max_iter,
          init=init,
          random_state=random_state,
          oversampling_factor=oversampling_factor,
          max_samples_per_batch=max_samples_per_batch,
          output_type="cupy",
      )
  
      cuml_kmeans.fit_predict(X)
  
imports: from cuml.internals.safe_imports import gpu_only_import
  from sklearn.preprocessing import StandardScaler
  from sklearn.metrics import adjusted_rand_score
  from sklearn import cluster
  from cuml.testing.utils import get_pattern, unit_param, quality_param, stress_param, array_equal
  from cuml.datasets import make_blobs
  import pytest
  import random
  import cuml
  import cuml.internals.logger
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 9 for calling a global function with example usage: test_all_kmeans_params(n_clusters, max_iter, init, oversampling_factor, max_samples_per_batch, random_state) without return types