code: def run_gapfilling_case(file_path):
      """
      The function runs an example of filling in gaps in a time series with
      air temperature. Real data case.
  
      :param file_path: path to the file
      :return: pandas dataframe with columns 'date','with_gap','ridge',
      'composite','temperature'
      """
  
      # Load dataframe
      full_path = os.path.join(str(fedot_project_root()), file_path)
      dataframe = pd.read_csv(full_path)
      dataframe["date"] = pd.to_datetime(dataframe["date"])
  
      # Filling in gaps based on inverted ridge regression model
      ridge_pipeline = get_simple_pipeline()
      ridge_gapfiller = ModelGapFiller(
          gap_value=-100.0, pipeline=ridge_pipeline
      )
      with_gap_array = np.array(dataframe["with_gap"])
      without_gap_arr_ridge = ridge_gapfiller.forward_inverse_filling(
          with_gap_array
      )
      dataframe["ridge"] = without_gap_arr_ridge
  
      # Filling in gaps based on a pipeline of 5 models
      composite_pipeline = get_composite_pipeline()
      composite_gapfiller = ModelGapFiller(
          gap_value=-100.0, pipeline=composite_pipeline
      )
      without_gap_composite = composite_gapfiller.forward_filling(
          with_gap_array
      )
      dataframe["composite"] = without_gap_composite
      return dataframe
  
file_path: FEDOT/cases/time_series_gapfilling_case.py
imports: import os
  import matplotlib.pyplot
  import numpy
  import pandas
  from sklearn.metrics import mean_absolute_error, mean_squared_error, median_absolute_error
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.utils import fedot_project_root
  from fedot.utilities.ts_gapfilling import ModelGapFiller
label_desc: Use image node_id 5 for calling a global function with example usage: run_gapfilling_case(file_path) and returns: dataframe