code: def initialize_lags(forecaster_name: str, lags: Any) -> np.ndarray:
      """
      Check lags argument input and generate the corresponding numpy ndarray.
  
      Parameters
      ----------
      forecaster_name : str
          Forecaster name. ForecasterAutoreg, ForecasterAutoregCustom,
          ForecasterAutoregDirect, ForecasterAutoregMultiSeries,
          ForecasterAutoregMultiSeriesCustom, ForecasterAutoregMultiVariate.
      lags : Any
          Lags used as predictors.
  
      Returns
      -------
      lags : numpy ndarray
          Lags used as predictors.
  
      """
  
      if isinstance(lags, int) and lags < 1:
          raise ValueError("Minimum value of lags allowed is 1.")
  
      if isinstance(lags, (list, np.ndarray)):
          for lag in lags:
              if not isinstance(lag, (int, np.int64, np.int32)):
                  raise TypeError("All values in `lags` must be int.")
  
      if isinstance(lags, (list, range, np.ndarray)) and min(lags) < 1:
          raise ValueError("Minimum value of lags allowed is 1.")
  
      if isinstance(lags, int):
          lags = np.arange(lags) + 1
      elif isinstance(lags, (list, range)):
          lags = np.array(lags)
      elif isinstance(lags, np.ndarray):
          lags = lags
      else:
          if not forecaster_name == "ForecasterAutoregMultiVariate":
              raise TypeError(
                  (
                      "`lags` argument must be an int, 1d numpy ndarray, range or list. "
                      f"Got {type(lags)}."
                  )
              )
          else:
              raise TypeError(
                  (
                      "`lags` argument must be a dict, int, 1d numpy ndarray, range or list. "
                      f"Got {type(lags)}."
                  )
              )
  
      return lags
  
imports: from typing import Union, Any, Optional, Tuple, Callable
  import warnings
  import importlib
  import joblib
  import numpy
  import pandas
  import sklearn
  import sklearn.pipeline
  import sklearn.linear_model
  from sklearn.compose import ColumnTransformer
  from sklearn.exceptions import NotFittedError
  import inspect
  from copy import deepcopy
  import skforecast
  from ..exceptions import MissingValuesExogWarning
  from ..exceptions import DataTypeWarning
  from ..exceptions import IgnoredArgumentWarning
  from ..exceptions import SkforecastVersionWarning
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_lags(forecaster_name, lags) and returns: lags