class_docstr: ``BaseBackend`` is the base class for all backends.
  All explainability implementations should extend this abstract class
  and implement the methods marked as abstract.
docstr: Get global contributions using the explainer data computed in the `run_explainer`
  method.
  
  Parameters
  ----------
  contributions : pd.DataFrame or list of pd.DataFrame
      The local contributions computed and aggregated by the backend.
  explain_data : dict, optional
      The data computed in the `run_explainer` method.
  subset : list
      list of indices on which to get local contributions.
  
  Returns
  -------
  pd.Series or list of pd.Series
      The global features importance computed by the backend.
docstr_tok: ["Get","global","contributions","using","the","explainer","data","computed","in","the","`","run_explainer","`","method",".","Parameters","--","--","--","--","--","contributions",":","pd.DataFrame","or","list","of","pd.DataFrame","The","local","contributions","computed","and","aggregated","by","the","backend",".","explain_data",":","dict",",","optional","The","data","computed","in","the","`","run_explainer","`","method",".","subset",":","list","list","of","indices","on","which","to","get","local","contributions",".","Returns","--","--","--","-","pd.Series","or","list","of","pd.Series","The","global","features","importance","computed","by","the","backend","."]
code: def get_global_features_importance(
      self,
      contributions: pd.DataFrame,
      explain_data: Optional[dict] = None,
      subset: Optional[List[int]] = None,
  ) -> Union[pd.Series, List[pd.Series]]:
      """Get global contributions using the explainer data computed in the `run_explainer`
      method.
  
      Parameters
      ----------
      contributions : pd.DataFrame or list of pd.DataFrame
          The local contributions computed and aggregated by the backend.
      explain_data : dict, optional
          The data computed in the `run_explainer` method.
      subset : list
          list of indices on which to get local contributions.
  
      Returns
      -------
      pd.Series or list of pd.Series
          The global features importance computed by the backend.
      """
      state = choose_state(contributions)
      if subset is not None:
          if isinstance(contributions, list):
              contributions = [c.loc[subset] for c in contributions]
          else:
              contributions = contributions.loc[subset]
      return state.compute_features_import(contributions)
  
file_path: shapash/shapash/backend/base_backend.py
imports: from abc import ABC, abstractmethod
  from typing import Any, List, Optional, Union
  import pandas
  import numpy
  from shapash.utils.check import check_model, check_contribution_object
  from shapash.utils.transform import adapt_contributions, get_preprocessing_mapping
  from shapash.utils.utils import choose_state
label_desc: Use image node_id 4 for calling the BaseBackend obj's underlying member method code with example usage: obj.get_global_features_importance(contributions, explain_data, subset) and returns: state