code: def load_forecaster(file_name: str, verbose: bool = True) -> object:
      """
      Load forecaster model using joblib. If the forecaster was saved with custom
      functions to create predictors or weights, these functions must be available
      in the environment where the forecaster is going to be loaded.
  
      Parameters
      ----------
      file_name: str
          Object file name.
      verbose: bool, default `True`
          Print summary about the forecaster loaded.
  
      Returns
      -------
      forecaster: forecaster
          Forecaster created with skforecast library.
  
      """
  
      forecaster = joblib.load(filename=file_name)
  
      skforecast_v = skforecast.__version__
      forecaster_v = forecaster.skforecast_version
  
      if forecaster_v != skforecast_v:
          warnings.warn(
              (
                  f"The skforecast version installed in the environment differs "
                  f"from the version used to create the forecaster.\n"
                  f"    Installed Version  : {skforecast_v}\n"
                  f"    Forecaster Version : {forecaster_v}\n"
                  f"This may create incompatibilities when using the library."
              ),
              SkforecastVersionWarning,
          )
  
      if verbose:
          forecaster.summary()
  
      return forecaster
  
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 21 for calling a global function with example usage: load_forecaster(file_name, verbose) and returns: forecaster