code: def get_arima_pipeline():
      """Function return complex pipeline with the following structure
      arima
      """
  
      node_1 = PipelineNode("arima")
      node_final = PipelineNode("linear", nodes_from=[node_1])
  
      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 2 for calling a global function with example usage: get_arima_pipeline() and returns: pipeline