code: def test_output_binary_classification_correct():
      """Check the correctness of prediction for binary classification task"""
  
      task_type = "classification"
  
      data = get_binary_classification_data()
  
      train_data, test_data = train_test_data_setup(data, shuffle=True)
  
      model = Fedot(problem=task_type, seed=1, timeout=0.1)
      model.fit(train_data, predefined_model="logit")
      model.predict(test_data)
      metrics = model.get_metrics(metric_names=["roc_auc", "f1"])
  
      assert all(value >= 0.6 for value in metrics.values())
  
imports: import logging
  from copy import deepcopy
  import pytest
  from examples.simple.classification.classification_pipelines import classification_pipeline_with_balancing, classification_pipeline_without_balancing
  from fedot import Fedot
  from fedot.api.api_utils.assumptions.assumptions_builder import AssumptionsBuilder
  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.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.preprocessing.preprocessing import DataPreprocessor
  from test.data.datasets import get_cholesterol_dataset
  from test.integration.api.test_main_api import get_dataset
  from test.unit.tasks.test_classification import get_binary_classification_data
label_desc: Use image node_id 2 for calling a global function with example usage: test_output_binary_classification_correct() without return types