code: def test_regression_pipeline_fit_predict_correct():
      data = get_synthetic_regression_data()
  
      pipeline = generate_pipeline()
      train_data, test_data = train_test_data_setup(data)
  
      pipeline.fit(input_data=train_data)
      _, rmse_on_test = get_rmse_value(pipeline, train_data, test_data)
  
      rmse_threshold = np.std(data.target) * 0.05
      assert rmse_on_test < rmse_threshold
      assert check_predict_correct(pipeline, train_data)
  
imports: import numpy
  import pytest
  from sklearn.datasets import make_regression
  from sklearn.metrics import mean_squared_error
  from fedot import Fedot
  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.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.composer.test_metrics import data_setup
label_desc: Use image node_id 6 for calling a global function with example usage: test_regression_pipeline_fit_predict_correct() without return types