code: def get_roc_auc_value(
      pipeline: Pipeline, train_data: InputData, test_data: InputData
  ) -> Tuple[float, float]:
      train_pred = pipeline.predict(input_data=train_data)
      test_pred = pipeline.predict(input_data=test_data)
      roc_auc_value_test = roc_auc(
          y_true=test_data.target, y_score=test_pred.predict
      )
      roc_auc_value_train = roc_auc(
          y_true=train_data.target, y_score=train_pred.predict
      )
  
      return roc_auc_value_train, roc_auc_value_test
  
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 2 for calling a global function with example usage: get_roc_auc_value(pipeline, train_data, test_data) and returns: roc_auc_value_train,  roc_auc_value_test