code: def get_simple_short_lagged_pipeline():
      # Create simple pipeline for forecasting
      node_lagged = PipelineNode("lagged")
      # Use 4 elements in time series as predictors
      node_lagged.parameters = {"window_size": 4}
      node_final = PipelineNode("linear", nodes_from=[node_lagged])
      pipeline = Pipeline(node_final)
  
      return pipeline
  
imports: import os
  import numpy
  import pandas
  from sklearn.metrics import mean_absolute_percentage_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.pipeline_builder import PipelineBuilder
  from fedot.core.pipelines.ts_wrappers import fitted_values
  from fedot.core.pipelines.ts_wrappers import in_sample_ts_forecast, out_of_sample_ts_forecast, in_sample_fitted_values
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 2 for calling a global function with example usage: get_simple_short_lagged_pipeline() and returns: pipeline