code: def get_multiple_ts_pipeline():
      node_filter_first = PipelineNode("smoothing")
      node_filter_first.parameters = {"window_size": 2}
      node_filter_second = PipelineNode("gaussian_filter")
      node_filter_second.parameters = {"sigma": 2}
  
      node_lagged = PipelineNode(
          "lagged", nodes_from=[node_filter_first, node_filter_second]
      )
      node_ridge = PipelineNode("ridge", nodes_from=[node_lagged])
      return Pipeline(node_ridge)
  
imports: from copy import deepcopy
  from typing import Optional
  import numpy
  import pandas
  import pytest
  from scipy import stats
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.arima import ARIMAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import AutoRegImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.ts_wrappers import in_sample_ts_forecast, out_of_sample_ts_forecast
  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
  from fedot.utilities.synth_dataset_generator import generate_synthetic_data
label_desc: Use image node_id 7 for calling a global function with example usage: get_multiple_ts_pipeline() and returns: Pipeline