code: def test_boosting_regression_operation():
      n_samples = 2000
      data = get_synthetic_regression_data(
          n_samples=n_samples, n_features=10, random_state=42
      )
      train_data, test_data = train_test_data_setup(data)
  
      model_names = OperationTypesRepository().suitable_operation(
          task_type=TaskTypesEnum.regression, tags=["boosting"]
      )
  
      for model_name in model_names:
          pipeline = PipelineBuilder().add_node(model_name).build()
          pipeline.fit(train_data, n_jobs=-1)
          predicted_output = pipeline.predict(test_data)
          metric = mean_squared_error(
              test_data.target, predicted_output.predict
          )
          rmse_threshold = np.std(test_data.target) ** 2
  
          assert isinstance(pipeline, Pipeline)
          assert predicted_output.predict.shape[0] == n_samples * 0.2
          assert metric < rmse_threshold
  
file_path: FEDOT/test/integration/models/test_strategy.py
imports: import numpy
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.metrics import roc_auc_score, 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.text import SkLearnTextVectorizeStrategy
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.pipeline_builder import PipelineBuilder
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from test.unit.pipelines.test_decompose_pipelines import get_classification_data
  from test.unit.tasks.test_regression import get_synthetic_regression_data
label_desc: Use image node_id 3 for calling a global function with example usage: test_boosting_regression_operation() without return types