code: def return_working_pipeline():
      node_lagged_1 = PipelineNode("lagged/1")
      node_exog = PipelineNode("exog_ts")
  
      node_final = PipelineNode(
          "ridge", nodes_from=[node_lagged_1, node_exog]
      )
      pipeline = Pipeline(node_final)
      return pipeline
  
file_path: FEDOT/examples/advanced/time_series_forecasting/nemo.py
imports: import warnings
  import numpy
  import pandas
  from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.data.data import InputData
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from copy import deepcopy
label_desc: Use image node_id 4 for calling a global function with example usage: return_working_pipeline() and returns: pipeline