code: def test_model_fit_correctly_but_random_predictions_on_test():
      """Checks whether the model can fit train dataset correctly, but
      the roc_auc_score on the test dataset is close to 0.5 (predictions are random).
      Test data has not relations between features and target."""
      train_data = get_synthetic_input_data(
          N_SAMPLES, N_FEATURES, random_state=1
      )
      test_data = get_random_target_data(train_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 8 for calling a global function with example usage: test_model_fit_correctly_but_random_predictions_on_test() without return types