code: def test_pipeline_with_clusters_fit_correct():
      mean_roc_on_test = 0
  
      # mean ROC AUC is analysed because of stochastic clustering
      for _ in range(5):
          data = get_synthetic_input_data(n_samples=10000)
  
          pipeline = generate_pipeline()
          train_data, test_data = train_test_data_setup(data)
  
          pipeline.fit(input_data=train_data)
          _, roc_on_test = get_roc_auc_value(
              pipeline, train_data, test_data
          )
          mean_roc_on_test = np.mean([mean_roc_on_test, roc_on_test])
  
      roc_threshold = 0.5
      assert mean_roc_on_test > roc_threshold
  
imports: import numpy
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from test.integration.models.test_split_train_test import get_roc_auc_value, get_synthetic_input_data
label_desc: Use image node_id 2 for calling a global function with example usage: test_pipeline_with_clusters_fit_correct() without return types