code: def get_fitted_fedot(forecast_length, train_data, **kwargs):
      params = {
          "problem": "ts_forecasting",
          "task_params": TsForecastingParams(
              forecast_length=forecast_length
          ),
          "seed": 1,
          "timeout": None,
          "pop_size": 50,
          "num_of_generations": 5,
          "with_tuning": False,
      }
      params.update(kwargs)
      fedot = Fedot(**params)
      fedot.fit(train_data)
      return fedot
  
file_path: FEDOT/test/integration/real_applications/test_model_result_reproducing.py
imports: from typing import List
  import numpy
  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.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
label_desc: Use image node_id 2 for calling a global function with example usage: get_fitted_fedot(forecast_length, train_data) and returns: fedot