code: def get_simple_pipeline(multi_data):
      """
      Pipeline looking like this
      lagged -> custom -> ridge
      """
  
      hist_list = []
      exog_list = []
  
      for i, data_id in enumerate(multi_data.keys()):
          if "exog_" in data_id:
              exog_list.append(PipelineNode(data_id))
          if "hist_" in data_id:
              lagged_node = PipelineNode(
                  "lagged", nodes_from=[PipelineNode(data_id)]
              )
              lagged_node.parameters = {"window_size": 2}
  
              hist_list.append(lagged_node)
  
      # For custom model params as initial approximation and model as function is necessary
      custom_node = PipelineNode("custom/empty", nodes_from=exog_list)
      custom_node.parameters = {
          "model_predict": model_predict,
          "model_fit": model_fit,
      }
  
      exog_pred_node = PipelineNode("exog_ts", nodes_from=[custom_node])
  
      final_ens = [exog_pred_node] + hist_list
      node_final = PipelineNode("ridge", nodes_from=final_ens)
      pipeline = Pipeline(node_final)
  
      return pipeline
  
imports: import logging
  import os
  import shutil
  from copy import deepcopy
  from typing import Any
  import numpy
  import pandas
  from examples.advanced.time_series_forecasting.custom_model_tuning import get_fitting_custom_pipeline
  from examples.simple.pipeline_import_export import create_correct_path
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  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 Task, TaskTypesEnum, TsForecastingParams
label_desc: Use image node_id 8 for calling a global function with example usage: get_simple_pipeline(multi_data) and returns: pipeline