code: def test_imputation_binary_features_with_equal_categories_correct():
      """
      The correctness of the gap-filling algorithm is checked on data with binary
      features. The number of known categories in each column is equal. Consequently,
      there is no possibility to insert the majority class label into the gaps.
      Instead of that the mean value is inserted.
      """
      nan_data = data_with_binary_int_features_and_equal_categories()
  
      imputation_node = PipelineNode("simple_imputation")
      imputation_node.fit(nan_data)
      predicted = imputation_node.predict(nan_data)
  
      assert np.isclose(predicted.predict[1, 0], 0.5)
      assert np.isclose(predicted.predict[1, 1], 5.0)
  
imports: import os
  import numpy
  import pytest
  from examples.simple.classification.classification_with_tuning import get_classification_dataset
  from examples.simple.regression.regression_with_tuning import get_regression_dataset
  from examples.simple.time_series_forecasting.gapfilling import generate_synthetic_data
  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.operations.evaluation.operation_implementations.data_operations.sklearn_imbalanced_class import ResampleImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import ImputationImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import CutImplementation, LaggedTransformationImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  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.operation_types_repository import OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.preprocessing.data_types import TYPE_TO_ID
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_only_categorical_features
label_desc: Use image node_id 23 for calling a global function with example usage: test_imputation_binary_features_with_equal_categories_correct() without return types