code: def test_gap_filling_forward_ridge_metric_correct():
      """
      Check that gap filling module allows to get appropriate error value into
      the filled parts
      """
      arr_with_gaps, real_values = get_array_with_gaps()
  
      # Find all gap indices in the array
      id_gaps = np.ravel(np.argwhere(arr_with_gaps == -100.0))
  
      ridge_pipeline = get_simple_ts_pipeline(model_root="ridge")
      gapfiller = ModelGapFiller(
          gap_value=-100.0, pipeline=ridge_pipeline
      )
      without_gap = gapfiller.forward_filling(arr_with_gaps)
  
      # Get only values in the gaps
      predicted_values = without_gap[id_gaps]
      true_values = real_values[id_gaps]
  
      rmse_test = mean_squared_error(
          true_values, predicted_values, squared=False
      )
  
      assert rmse_test < 1.0
  
imports: import numpy
  import pytest
  from sklearn.metrics import mean_squared_error
  from typing import Callable
  from examples.simple.time_series_forecasting.gapfilling import get_array_with_gaps
  from fedot.utilities.ts_gapfilling import ModelGapFiller, SimpleGapFiller
  from test.unit.tasks.test_forecasting import get_simple_ts_pipeline
label_desc: Use image node_id 18 for calling a global function with example usage: test_gap_filling_forward_ridge_metric_correct() without return types