code: def test_fit_transform_weighted_kmeans(
      nrows, ncols, nclusters, max_weight, random_state
  ):
      # Using fairly high variance between points in clusters
      cluster_std = 1.0
      np.random.seed(random_state)
  
      # set weight per sample to be from 1 to max_weight
      wt = np.random.randint(1, high=max_weight, size=nrows)
  
      X, y = make_blobs(
          nrows,
          ncols,
          nclusters,
          cluster_std=cluster_std,
          shuffle=False,
          random_state=0,
      )
  
      cuml_kmeans = cuml.KMeans(
          init="k-means++",
          n_clusters=nclusters,
          n_init=10,
          random_state=random_state,
          output_type="numpy",
      )
  
      cuml_transf = cuml_kmeans.fit_transform(X, sample_weight=wt)
      cu_score = cuml_kmeans.score(X)
  
      sk_kmeans = cluster.KMeans(
          random_state=random_state, n_clusters=nclusters
      )
      sk_transf = sk_kmeans.fit_transform(
          cp.asnumpy(X), sample_weight=wt
      )
      sk_score = sk_kmeans.score(cp.asnumpy(X))
  
      assert abs(cu_score - sk_score) <= cluster_std * 1.5
      assert sk_transf.shape == cuml_transf.shape
  
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 11 for calling a global function with example usage: test_fit_transform_weighted_kmeans(nrows, ncols, nclusters, max_weight, random_state) without return types