code: def run_import_export_example(pipeline_path, pipeline):
      features_options = {"informative": 1, "bias": 0.0}
      samples_amount = 100
      features_amount = 2
      x_train, y_train, x_test, y_test = get_regression_dataset(
          features_options, samples_amount, features_amount
      )
  
      # Define regression task
      task = Task(TaskTypesEnum.regression)
  
      # Prepare data to train the model
      train_input = InputData(
          idx=np.arange(0, len(x_train)),
          features=x_train,
          target=y_train,
          task=task,
          data_type=DataTypesEnum.table,
      )
  
      predict_input = InputData(
          idx=np.arange(0, len(x_test)),
          features=x_test,
          target=None,
          task=task,
          data_type=DataTypesEnum.table,
      )
  
      # Get pipeline and fit it
      pipeline.fit_from_scratch(train_input)
  
      predicted_output = pipeline.predict(predict_input)
      prediction_before_export = np.array(predicted_output.predict)
      print(f"Before export {prediction_before_export[:4]}")
  
      # Export it
      path_to_save_and_load = (
          f"{fedot_project_root()}/examples/simple/{pipeline_path}"
      )
      pipeline.save(
          path=path_to_save_and_load,
          create_subdir=False,
          is_datetime_in_path=False,
      )
  
      # Import pipeline
      new_pipeline = Pipeline().load(path_to_save_and_load)
  
      predicted_output_after_export = new_pipeline.predict(
          predict_input
      )
      prediction_after_export = np.array(
          predicted_output_after_export.predict
      )
  
      print(f"After import {prediction_after_export[:4]}")
  
      dict_pipeline, dict_fitted_operations = pipeline.save()
      dict_pipeline = json.loads(dict_pipeline)
      pipeline_from_dict = Pipeline.from_serialized(
          dict_pipeline, dict_fitted_operations
      )
  
      predicted_output = pipeline_from_dict.predict(predict_input)
      prediction = np.array(predicted_output.predict)
      print(
          f"Prediction from pipeline loaded from dict {prediction[:4]}"
      )
  
file_path: FEDOT/examples/simple/pipeline_import_export.py
imports: import json
  import os
  import numpy
  from examples.simple.regression.regression_with_tuning import get_regression_dataset
  from examples.simple.regression.regression_pipelines import regression_ransac_pipeline
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 2 for calling a global function with example usage: run_import_export_example(pipeline_path, pipeline) without return types