code: def test_classification_with_pca_pipeline_fit_correct():
      data = classification_dataset_with_redundant_features()
      pipeline_pca = pipeline_with_pca()
      pipeline = pipeline_simple()
  
      train_data, test_data = train_test_data_setup(data, shuffle=True)
  
      pipeline.fit(input_data=train_data)
      pipeline_pca.fit(input_data=train_data)
  
      results = pipeline.predict(input_data=test_data)
      results_pca = pipeline_pca.predict(input_data=test_data)
  
      roc_auc_on_test = roc_auc(
          y_true=test_data.target,
          y_score=results.predict,
          multi_class="ovo",
          average="macro",
      )
  
      roc_auc_on_test_pca = roc_auc(
          y_true=test_data.target,
          y_score=results_pca.predict,
          multi_class="ovo",
          average="macro",
      )
  
      assert roc_auc_on_test_pca > roc_auc_on_test * 0.95 > 0.5
  
imports: import os
  import numpy
  from sklearn.datasets import load_iris, make_classification
  from sklearn.metrics import roc_auc_score
  from examples.simple.classification.image_classification_problem import run_image_classification_problem
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  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
  from test.integration.models.test_model import classification_dataset_with_redundant_features
label_desc: Use image node_id 8 for calling a global function with example usage: test_classification_with_pca_pipeline_fit_correct() without return types