code: def test_regression_multiscale_pipeline_forecast_correct():
      train_data, test_data = get_ts_data(forecast_length=5)
  
      pipeline = get_multiscale_pipeline(2, 4)
      pipeline.fit(input_data=train_data)
      test_pred = pipeline.predict(input_data=test_data)
  
      # Calculate metric
      test_pred = np.ravel(np.array(test_pred.predict))
      test_target = np.ravel(np.array(test_data.target))
      rmse_test = mean_squared_error(
          test_target, test_pred, squared=False
      )
  
      rmse_threshold = _max_rmse_threshold_by_std(
          test_data.target, is_strict=True
      )
  
      assert rmse_test < rmse_threshold
  
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 12 for calling a global function with example usage: test_regression_multiscale_pipeline_forecast_correct() without return types