code: def test_presets_regression():
      task = Task(TaskTypesEnum.regression)
  
      regr_operations = get_operations_for_task(task=task, mode="all")
  
      preset_best_quality = OperationsPreset(
          task=task, preset_name="best_quality"
      )
      operations_for_best_quality = (
          preset_best_quality.filter_operations_by_preset()
      )
  
      preset_fast_train = OperationsPreset(
          task=task, preset_name="fast_train"
      )
      operations_for_fast_train = (
          preset_fast_train.filter_operations_by_preset()
      )
  
      preset_auto = OperationsPreset(task=task, preset_name="auto")
      operations_for_auto = preset_auto.filter_operations_by_preset()
  
      assert (
          len(operations_for_fast_train)
          < len(operations_for_best_quality)
          == len(regr_operations)
          == len(operations_for_auto)
      )
      assert {"dtreg", "lasso", "ridge", "linear"} <= set(
          operations_for_fast_train
      )
  
file_path: FEDOT/test/unit/api/test_presets.py
imports: from fedot import Fedot
  from fedot.api.api_utils.api_params_repository import ApiParamsRepository
  from fedot.api.api_utils.presets import OperationsPreset
  from fedot.core.constants import FAST_TRAIN_PRESET_NAME
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.operation_types_repository import OperationTypesRepository, get_operations_for_task
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from test.data.datasets import data_with_binary_features_and_categorical_target
label_desc: Use image node_id 2 for calling a global function with example usage: test_presets_regression() without return types