code: def get_ts_pipelines_for_testing():
      """Generate simple specific pipelines for testing"""
      node_lagged = PipelineNode("sparse_lagged")
      node_final = PipelineNode("linear", nodes_from=[node_lagged])
      sparse_lagged_pipeline = Pipeline(node_final)
  
      node_lagged = PipelineNode("lagged")
      node_final = PipelineNode("linear", nodes_from=[node_lagged])
      lagged_pipeline = Pipeline(node_final)
  
      arima_pipeline = Pipeline(PipelineNode("arima"))
      return arima_pipeline, sparse_lagged_pipeline, lagged_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 3 for calling a global function with example usage: get_ts_pipelines_for_testing() and returns: arima_pipeline,  sparse_lagged_pipeline,  lagged_pipeline