code: def get_feature_importances_filtered(
      coef, feature_names, flt_indices, top, coef_std=None
  ):
      # type: (...) -> FeatureImportances
      if flt_indices is not None:
          coef = coef[flt_indices]
          if coef_std is not None:
              coef_std = coef_std[flt_indices]
  
      indices = argsort_k_largest(coef, top)
      names, values = feature_names[indices], coef[indices]
      std = None if coef_std is None else coef_std[indices]
      return FeatureImportances.from_names_values(
          names,
          values,
          std,
          remaining=coef.shape[0] - len(indices),
      )
  
imports: from eli5.base import FeatureImportances, Explanation
  from eli5.sklearn.utils import get_feature_names_filtered
  from eli5.utils import argsort_k_largest
label_desc: Use image node_id 1 for calling a global function with example usage: get_feature_importances_filtered(coef, feature_names, flt_indices, top, coef_std) and returns: FeatureImportances