code: def test_compose_fedot_model_without_tuning():
      task_type = "classification"
      train_input, _, _ = get_dataset(task_type=task_type)
  
      model = Fedot(
          problem=task_type,
          timeout=0.1,
          preset="fast_train",
          with_tuning=True,
      )
      model.fit(train_input)
  
      assert not model.api_composer.was_tuned
  
imports: import logging
  from copy import deepcopy
  import pytest
  from examples.simple.classification.classification_pipelines import classification_pipeline_with_balancing, classification_pipeline_without_balancing
  from fedot import Fedot
  from fedot.api.api_utils.assumptions.assumptions_builder import AssumptionsBuilder
  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.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.preprocessing.preprocessing import DataPreprocessor
  from test.data.datasets import get_cholesterol_dataset
  from test.integration.api.test_main_api import get_dataset
  from test.unit.tasks.test_classification import get_binary_classification_data
label_desc: Use image node_id 1 for calling a global function with example usage: test_compose_fedot_model_without_tuning() without return types