code: def display_metrics(test_part, predicted_values, pipeline_name):
      mse = mean_squared_error(
          test_part.target, predicted_values, squared=False
      )
      mae = mean_absolute_error(test_part.target, predicted_values)
      print(f"RMSE {pipeline_name} - {mse:.4f}")
      print(f"MAE {pipeline_name} - {mae:.4f}\n")
  
file_path: FEDOT/examples/advanced/decompose/refinement_forecast_example.py
imports: import warnings
  import numpy
  import pandas
  from matplotlib import pyplot
  from sklearn.metrics import mean_squared_error, mean_absolute_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.ts_wrappers import in_sample_ts_forecast
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import TaskTypesEnum, Task, TsForecastingParams
  from fedot.core.utils import set_random_seed
label_desc: Use image node_id 5 for calling a global function with example usage: display_metrics(test_part, predicted_values, pipeline_name) without return types