code: def explain_weights(estimator, **kwargs):
      """Return an explanation of estimator parameters (weights).
  
      :func:`explain_weights` is not doing any work itself, it dispatches
      to a concrete implementation based on estimator type.
  
      Parameters
      ----------
      estimator : object
          Estimator instance. This argument must be positional.
  
      top : int or (int, int) tuple, optional
          Number of features to show. When ``top`` is int, ``top`` features with
          a highest absolute values are shown. When it is (pos, neg) tuple,
          no more than ``pos`` positive features and no more than ``neg``
          negative features is shown. ``None`` value means no limit.
  
          This argument may be supported or not, depending on estimator type.
  
      target_names : list[str] or {'old_name': 'new_name'} dict, optional
          Names of targets or classes. This argument can be used to provide
          human-readable class/target names for estimators which don't expose
          clss names themselves. It can be also used to rename estimator-provided
          classes before displaying them.
  
          This argument may be supported or not, depending on estimator type.
  
      targets : list, optional
          Order of class/target names to show. This argument can be also used
          to show information only for a subset of classes. It should be a list
          of class / target names which match either names provided by
          an estimator or names defined in ``target_names`` parameter.
  
          This argument may be supported or not, depending on estimator type.
  
      feature_names : list, optional
          A list of feature names. It allows to specify feature
          names when they are not provided by an estimator object.
  
          This argument may be supported or not, depending on estimator type.
  
      feature_re : str, optional
          Only feature names which match ``feature_re`` regex are returned
          (more precisely, ``re.search(feature_re, x)`` is checked).
  
      feature_filter : Callable[[str], bool], optional
          Only feature names for which ``feature_filter`` function returns True
          are returned.
  
      **kwargs: dict
          Keyword arguments. All keyword arguments are passed to
          concrete explain_weights... implementations.
  
      Returns
      -------
      Explanation
          :class:`~Explanation` result. Use one of the formatting functions from
          :mod:`eli5.formatters` to print it in a human-readable form.
  
          Explanation instances have repr which works well with
          IPython notebook, but it can be a better idea to use
          :func:`eli5.show_weights` instead of :func:`eli5.explain_weights`
          if you work with IPython: :func:`eli5.show_weights` allows to customize
          formatting without a need to import :mod:`eli5.formatters` functions.
      """
      return Explanation(
          estimator=repr(estimator),
          error="estimator %r is not supported" % estimator,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: explain_weights(estimator) and returns: Explanation