code: def _target_feature_weights(
      feature_weights_dict, num_features, bias_idx
  ):
      feature_weights = np.zeros(num_features)
      for k, v in feature_weights_dict.items():
          if k is None:
              feature_weights[bias_idx] = v
          else:
              feature_weights[k] = v
      return feature_weights
  
file_path: eli5/eli5/lightgbm.py
imports: from __future__ import absolute_import, division
  from collections import defaultdict
  from typing import DefaultDict, Optional
  import numpy
  import lightgbm
  from eli5.explain import explain_weights, explain_prediction
  from eli5._feature_importances import get_feature_importance_explanation
  from eli5.sklearn.utils import handle_vec, get_X, get_X0, add_intercept, predict_proba
  from eli5._decision_path import get_decision_path_explanation
label_desc: Use image node_id 10 for calling a global function with example usage: _target_feature_weights(feature_weights_dict, num_features, bias_idx) and returns: feature_weights