code: def test_fine_tune_atomized_model_correct():
      train_data, test_data = create_input_data()
  
      atm_model = create_atomized_model()
      dummy_atomized_model = create_atomized_model()
  
      fine_tuned_atomized_model = atm_model.fine_tune(
          metric_function=RMSE.get_value,
          input_data=train_data,
          iterations=5,
          timeout=1,
      )
      dummy_atomized_model.fit(None, train_data)
  
      fitted_dummy_model, _ = dummy_atomized_model.fit(None, train_data)
      (
          fitted_fine_tuned_atomized_model,
          _,
      ) = fine_tuned_atomized_model.fit(None, train_data)
  
      after_tuning_output = fine_tuned_atomized_model.predict(
          fitted_fine_tuned_atomized_model, data=test_data
      )
      after_tuning_predicted = after_tuning_output.predict
      before_tuning_output = dummy_atomized_model.predict(
          fitted_dummy_model, data=test_data
      )
      before_tuning_predicted = before_tuning_output.predict
  
      aft_tun_mse = mean_squared_error(
          y_true=test_data.target, y_pred=after_tuning_predicted
      )
      bfr_tun_mse = mean_squared_error(
          y_true=test_data.target, y_pred=before_tuning_predicted
      )
  
      deviation = 0.50 * bfr_tun_mse
      assert aft_tun_mse <= (bfr_tun_mse + deviation)
  
imports: import json
  import os
  from functools import reduce
  import numpy
  import pytest
  from sklearn.metrics import mean_squared_error
  from fedot.core.composer.metrics import RMSE
  from fedot.core.data.data import InputData
  from fedot.core.operations.atomized_model import AtomizedModel
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.utils import fedot_project_root
  from test.integration.utilities.test_pipeline_import_export import create_correct_path, create_func_delete_files
label_desc: Use image node_id 13 for calling a global function with example usage: test_fine_tune_atomized_model_correct() without return types