code: def test_save_load_fitted_atomized_pipeline_correctly():
      train_data, test_data = create_input_data()
  
      pipeline = create_pipeline_with_several_nested_atomized_model()
  
      pipeline.fit(train_data)
      before_save_predicted = pipeline.predict(test_data)
      json_actual, _ = pipeline.save(
          "test_save_load_fitted_atomized_pipeline_correctly",
          create_subdir=False,
      )
  
      json_path_load = create_correct_path(
          "test_save_load_fitted_atomized_pipeline_correctly"
      )
  
      pipeline_loaded = Pipeline.from_serialized(json_path_load)
      json_expected, _ = pipeline_loaded.save(
          "test_save_load_fitted_atomized_pipeline_correctly_loaded",
          create_subdir=False,
      )
  
      assert pipeline.length == pipeline_loaded.length
      assert json_actual == json_expected
  
      pipeline_loaded.fit_from_scratch(train_data)
      after_save_predicted = pipeline_loaded.predict(test_data)
  
      bfr_save_mse = mean_squared_error(
          y_true=test_data.target, y_pred=before_save_predicted.predict
      )
      aft_load_mse = mean_squared_error(
          y_true=test_data.target, y_pred=after_save_predicted.predict
      )
  
      assert np.isclose(aft_load_mse, bfr_save_mse)
  
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 10 for calling a global function with example usage: test_save_load_fitted_atomized_pipeline_correctly() without return types