code: def load_model(bento_model: str | Tag | Model) -> SklearnModel:
      """
      Load the scikit-learn model with the given tag from the local BentoML model store.
  
      Args:
          bento_model: Either the tag of the model to get from the store, or a BentoML `~bentoml.Model`
                       instance to load the model from.
  
      Returns:
          The scikit-learn model loaded from the model store or BentoML :obj:`~bentoml.Model`.
  
      Example:
  
      .. code-block:: python
  
          import bentoml
          sklearn = bentoml.sklearn.load_model('my_model:latest')
      """  # noqa
      if not isinstance(bento_model, Model):
          bento_model = get(bento_model)
  
      if bento_model.info.module not in (MODULE_NAME, __name__):
          raise NotFound(
              f"Model {bento_model.tag} was saved with module {bento_model.info.module}, not loading with {MODULE_NAME}."
          )
      model_file = bento_model.path_of(MODEL_FILENAME)
  
      return joblib.load(model_file)
  
file_path: BentoML/grpc-client/bentoml/_internal/frameworks/sklearn.py
imports: from __future__ import annotations
  import logging
  import typing
  from types import ModuleType
  from typing import TYPE_CHECKING
  import bentoml
  from bentoml import Tag
  from bentoml.exceptions import MissingDependencyException
  from bentoml.exceptions import NotFound
  from bentoml.models import Model
  from bentoml.models import ModelContext
  from ..types import LazyType
  from ..utils.pkg import get_pkg_version
label_desc: Use image node_id 2 for calling a global function with example usage: load_model(bento_model) and returns: joblib