code: def test_model_predictions_on_train_test_random():
      """Checks that model can't predict correctly on random train and test datasets and
      the roc_auc_scores is close to 0.5.
      Both train and test data have no relations between features and target.
      """
      data = get_synthetic_input_data(
          N_SAMPLES, N_FEATURES, random_state=1
      )
      data = get_random_target_data(data)
  
      train_data, test_data = train_test_data_setup(data)
  
      pipeline = generate_pipeline()
      pipeline.fit(input_data=train_data)
      roc_auc_value_train, roc_auc_value_test = get_roc_auc_value(
          pipeline, train_data, test_data
      )
      train_auc_thr = get_auc_threshold(roc_auc_value_train)
      test_auc_thr = get_auc_threshold(roc_auc_value_test)
  
      assert test_auc_thr <= CORRECT_MODEL_AUC_THR
      assert train_auc_thr <= CORRECT_MODEL_AUC_THR
  
imports: import random
  from copy import deepcopy
  from typing import Tuple
  import numpy
  from sklearn.datasets import make_classification
  from sklearn.metrics import roc_auc_score
  from fedot.core.data.data import InputData
  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 fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
label_desc: Use image node_id 9 for calling a global function with example usage: test_model_predictions_on_train_test_random() without return types