code: def test_composing_with_custom_model():
      train_data, test_data = prepare_data()
  
      initial_assumption = get_simple_pipeline(train_data)
      automl = Fedot(
          problem="ts_forecasting",
          timeout=0.1,
          task_params=TsForecastingParams(forecast_length=5),
          logging_level=logging.DEBUG,
          initial_assumption=initial_assumption,
          preset="ts",
      )
      pipeline = automl.fit(train_data)
  
      pipeline.fit_from_scratch(train_data)
      predicted_test = pipeline.predict(test_data)
  
      assert predicted_test is not None
  
imports: import logging
  import os
  import shutil
  from copy import deepcopy
  from typing import Any
  import numpy
  import pandas
  from examples.advanced.time_series_forecasting.custom_model_tuning import get_fitting_custom_pipeline
  from examples.simple.pipeline_import_export import create_correct_path
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
label_desc: Use image node_id 13 for calling a global function with example usage: test_composing_with_custom_model() without return types