code: def test_nans_columns_process_correctly():
      """Check if data with nans processed correctly. Columns with nans should be ignored"""
      pipeline = Pipeline(PipelineNode("ridge"))
      data_with_nans = data_with_too_much_nans()
  
      pipeline = correct_preprocessing_params(
          pipeline, categorical_max_uniques_th=5
      )
      pipeline.fit(data_with_nans)
  
      # Ridge should use only one feature to make prediction
      fitted_ridge = pipeline.nodes[0]
      coefficients = fitted_ridge.operation.fitted_operation.coef_
      coefficients_shape = coefficients.shape
  
      assert 1 == coefficients_shape[1]
  
imports: import numpy
  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 TaskTypesEnum, Task
  from fedot.preprocessing.categorical import BinaryCategoricalPreprocessor
  from fedot.preprocessing.data_types import TableTypesCorrector
  from fedot.preprocessing.structure import DEFAULT_SOURCE_NAME
  from test.unit.data_operations.test_data_operations_implementations import get_mixed_data
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_only_categorical_features, data_with_too_much_nans, data_with_spaces_and_nans_in_features, data_with_nans_in_target_column, data_with_nans_in_multi_target, data_with_categorical_target
label_desc: Use image node_id 4 for calling a global function with example usage: test_nans_columns_process_correctly() without return types