code: def test_auto_preset_converted_correctly():
      """Checks that the proposed method of automatic preset detection correctly converts a preset"""
      tiny_timeout_value = 0.001
      large_pop_size = 500
      data = data_with_binary_features_and_categorical_target()
  
      simple_init_assumption = Pipeline(PipelineNode("logit"))
      fedot_model = Fedot(
          problem="classification",
          preset="auto",
          timeout=tiny_timeout_value,
          initial_assumption=simple_init_assumption,
          pop_size=large_pop_size,
          with_tuning=False,
      )
      # API must return initial assumption without composing and tuning (due to population size is too large)
      fedot_model.fit(data)
      assert fedot_model.params.get("preset") == FAST_TRAIN_PRESET_NAME
  
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 5 for calling a global function with example usage: test_auto_preset_converted_correctly() without return types