code: def test_lagged_with_invalid_params_fit_correctly():
      """The function define a pipeline with incorrect parameters in the lagged
      transformation. During the training of the pipeline, the parameter 'window_size'
      is corrected
      """
      window_size = 600
      len_forecast = 50
  
      # The length of the time series is 500 elements
      file_path = fedot_project_root().joinpath(
          "test/data/short_time_series.csv"
      )
      df = pd.read_csv(file_path)
      time_series = np.array(df["sea_height"])
  
      task = Task(
          TaskTypesEnum.ts_forecasting,
          TsForecastingParams(forecast_length=len_forecast),
      )
  
      ts_input = InputData(
          idx=np.arange(0, len(time_series)),
          features=time_series,
          target=time_series,
          task=task,
          data_type=DataTypesEnum.ts,
      )
  
      # Get pipeline with lagged transformation in it
      pipeline = get_ts_pipeline(window_size)
  
      # Fit it
      pipeline.fit(ts_input)
      assert (
          1
          <= pipeline.nodes[-1].parameters["window_size"]
          <= len(time_series) - len_forecast
      )
  
file_path: FEDOT/test/unit/data_operations/test_data_operation_params.py
imports: from copy import copy
  import numpy
  import pandas
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  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
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 3 for calling a global function with example usage: test_lagged_with_invalid_params_fit_correctly() without return types