code: def test_boosting_classification_operation():
      train_data, test_data = get_classification_data()
  
      model_names = OperationTypesRepository().suitable_operation(
          task_type=TaskTypesEnum.classification, tags=["boosting"]
      )
  
      for model_name in model_names:
          pipeline = (
              PipelineBuilder()
              .add_node(model_name, params={"n_jobs": -1})
              .build()
          )
          pipeline.fit(train_data)
          predicted_output = pipeline.predict(
              test_data, output_mode="labels"
          )
          metric = roc_auc(test_data.target, predicted_output.predict)
  
          assert isinstance(pipeline, Pipeline)
          assert predicted_output.predict.shape[0] == 240
          assert metric > 0.5
  
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 2 for calling a global function with example usage: test_boosting_classification_operation() without return types